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Resources, Conservation & Recycling

journal homepage: www.elsevier.com/locate/resconrec

Reverse logistics and closed-loop supply chain of Waste Electrical and Electronic Equipment (WEEE)/E-waste: A comprehensive literature review

Md Tasbirul Islam⁎, Nazmul Huda⁎

School of Engineering, Macquarie University, NSW 2109, Australia

A R T I C L E I N F O

Keywords: Reverse logistics (RL) Closed-loop supply chain (CLSC) Waste Electrical and Electronic Equipment (WEEE) E-waste management Literature review Sustainability Circular economy

A B S T R A C T

Reverse logistics (RL) and the closed-loop supply chain (CLSC) are integral parts of the holistic waste man- agement process. One of the important end-of-life (EOL) products considered in the RL/CLSC is Waste Electrical and Electronic Equipment (WEEE)/E-waste. Numerous research papers were published in the RL and CLSC disciplines focusing WEEE separately. However, there is no single review article found on the product-specific issues. To bridge this gap, a total of 157 papers published between 1999 and May 2017 were selected, cate- gorized, analyzed using content analysis method. The method involves four steps: material collection, de- scriptive analysis, category selection and material evaluation. For the systematic literature review, the steps were followed and four main types of research in the field of RL and CLSC of E-waste, namely designing and planning of reverse distribution, decision making and performance evaluation, conceptual framework, and qualitative studies were identified and reviewed. Research gaps in literature were diagnosed to suggest future research opportunities. The review first of its kind that may provide a useful reference for academicians, re- searchers and industry practitioners for a better understanding of WEEE focused RL/CLSC activities and re- search.

1. Introduction

Due to growing environmental regulations, potential recovery of valuable material resources for the secondary market, and sustainable business practices, over the last twenty years, the concept of reverse logistics (RL) has been accepted and widely practiced in manufacturing industries all over the world. The definition of RL according to Stock (1992) refers to “… the term often used for the role of logistics in re- cycling, waste disposal and management of hazardous materials; a broader perspective includes all issues relating to logistics activities carried out in source reduction, recycling, substitution, reuse of mate- rials and disposal”. This definition links directly RL activities in a waste management scenario that provides a more holistic approach to re- source conservation and recycling of end-of-life (EOL) products. As waste generation by various industries is increasing at a skyrocketing pace, many governments across the globe compel the producer/man- ufacturer to implement the extended producer responsibility (EPR) principle. According to the Organisation for Economic Co-operation and Development (OECD), ‘’EPR is a policy approach under which producers are given a significant responsibility – financial and/or physical – for the treatment or disposal of post-consumer products’’ (OECD, 2017). With this instrument, manufacturers now have to

develop a sustainable reverse supply chain (RSC) besides the conven- tional forward logistics (FL) system. According to Stevens (1989), a forward supply chain (FSC) is’ ’a system consisting of material sup- pliers, production facilities, distribution services, and customers who are all linked together via the downstream feed-forward flow of mate- rials (deliveries) and the upstream feedback flow of information (or- ders)’’. On the other hand, when the FSC and RSC systems are con- sidered in an integrated manner, the concept of the closed-loop supply chain (CLSC) evolved. It considers efficient product return management and conducts value recovery activities so that secondary materials can be used as input for ‘’new’’ customer product. Rather considering legal, social responsibilities, or even operational and technical details of the FSC and RSC, the CLSC focuses explicitly on business perspectives of the supply chains. According to Guide and Van Wassenhove (2009), ‘’CLSC management is the design, control, and operation of a system to max- imize value creation over the entire life cycle of a product with dynamic recovery of value from different types and volumes of returns over time’’. From the sustainability viewpoint in all three dimensions – so- cial, economic and environmental - in conjunction with the circular economy, RL/CLSC is an emerging area of research that attracts both academic and industry practitioners. According to Geissdoerfer et al. (2017), ‘’ A circular economy is a regenerative system in which resource

https://doi.org/10.1016/j.resconrec.2018.05.026 Received 20 November 2017; Received in revised form 21 March 2018; Accepted 24 May 2018

⁎ Corresponding authors. E-mail addresses: [email protected] (M.T. Islam), [email protected] (N. Huda).

Resources, Conservation & Recycling 137 (2018) 48–75

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input and waste, emission, and energy leakage are minimized by slowing, closing, and narrowing the material and energy loops. This can be achieved through long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling’’ and sustainability is de- fined as the balanced integration of economic performance, social in- clusiveness, and environmental resilience, to the benefit of current and future generations. Based on the above definition of RL/CLSC, the generic diagram can be illustrated as in Fig. 1.

Among the various EOL products identified in RL and CLSC re- search, E-waste is found as a significant one. The question is how dif- ferent is the RL and CLSC systems from a generic form when WEEE is considered. A lot of previously published papers have not clearly spe- cified the difference which is a drawback of some of the earlier studies.

E-waste possesses some special characteristics and features that make its RL and CLSC systems unique from general RL and CLSC sys- tems. WEEE is one of the fastest-growing streams at present due to a shorter product lifecycle (PLC) and rapidly changing customer attitudes towards disposing of them (Islam et al., 2016; Nnorom and Osibanjo, 2008). According to “Global E-waste Monitor Report 2017” published by United Nations University (UNU), in the year 2016, 44.7 million tonnes (Mt) of e-waste was generated in the world and only 20% was recycled through proper channels (Baldé et al., 2017). This generation volume is significant compared to other EOL items. For example, every year, only 8 to 9 million tonnes of end-of-life vehicle (ELV) is generated (Eurostat, 2018) which is 5 times less than the WEEE generation. Globally, to tackle the emerging waste stream under comprehensive WEEE management policies, several countries implemented regulations towards minimizing the negative environmental impact and prioritizing valuable resource recovery. To bind all the stakeholders legally in managing E-waste, European Union (EU) is at the forefront. On 13th

August, 2012, the EU WEEE DIRECTIVE 2012/19/EU came into force by which member countries in the EU are obliged to follow the recovery and recycling target implementing EPR policy. According to the Di- rective, E-waste is divided into ten different categories (until 15 August 2018) (Directive, 2012). Table 1 shows WEEE product categories with target recovery and recycling rate.

In principle, complex processes of RL and CLSC start with the dis- posal of EOL electrical and electronic equipment (EEE). However, in WEEE’s return management, multiple factors along with a higher de- gree of uncertainties such as quality, quantity and time are involved (Chen and He, 2010). First, the huge amount of generation is coming from three distinct sources: households, government and institutions, and businesses (Li et al., 2006). Households dispose of a range of equipment starting from large household equipment like refrigerators, washing machines to small consumer electronics, mobile phone; whereas information and communication technology (ICT) equipment is largely discarded by organizations. On the other hand, for the same equipment, average lifespan varies significantly. Second, the method of E-waste collection from the sources varies substantially in terms of collection points (e.g. municipality collection points, retailers, product manufacturers, EEE repairs, third party recycling service provider companies etc.) involved in a EOL-WEEE recovery process (Iacovidou et al., 2017). For instance, households can discard their E-waste in a number of ways: 1) at the municipal collection points, 2) leave it to their kerbside, 3) drop it off at special events, 4) return back to re- tailers/ point of purchase, and 5) return back to manufacturers/re- cyclers appointed by manufacturer. For business and other organiza- tions, leasing became increasingly popular and in this process, leasing companies are responsible for EOL dispositions which further involve RL service providers for transportation, local recyclers and small busi- nesses that deal with reuse of EEE items. Disposing E-waste to perma- nent drop-off locations is also practiced by institutions. Third, collected quantities then transported to treatment facilities where WEEE goes through testing, inspection, and sorting and dissembled according to specific product categories before transferred for processing. An opti- mized network design plays a crucial role in efficient and successful RL processes. For example, in Switzerland, three take-back systems, SWICO, SENS, Swiss Lighting Recycling Foundation (SLRS) together established 6000 collection points by which 95% of the E-waste is collected and recycled (SWICO, 2017). Fourth, depending upon the material content and value proposition (i.e. quality of waste), five dif- ferent disposition alternatives (e.g. reuse, repair, remanufacture and

Fig. 1. Generic diagram of CLSC including forward and reverse flow, adapted from Chopra and Meindl (2007).

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

recycling) exist which is often problematic selecting the best available alternative. Top management of computer hardware industries strug- gles to evaluate the ultimate fate of EOL-computers (Ravi et al., 2005a, 2005b). Large quantities of E-waste is also disposed of in landfills. Compared to other EOL products, E-waste has a complex material structure containing both environmentally hazardous substances (i.e. mercury, cadmium, lead, chromium, poly/brominated flame re- tardants, ozone-depleting chemicals such as CFC etc.) and valuable critical raw materials (CRM), such as copper and gold (Kumar et al., 2017). Physical and mechanical processing supply secondary materials recovered from WEEE to the EEE and other industries (Işıldar et al., 2017). Thus, RL and CLSC of E-waste are very unique in terms of as- sociated collection and EOL options involved. Fig. 2 shows the CLSC diagram of E-waste.

The number of international peer-reviewed articles published on RL/CLSC issues focusing on WEEE is increasing considerably. However, no single review has yet been conducted to summarize all the relevant articles with a product-specific focus. To the best of the authors’ knowledge, this is the first attempt at reviewing RL/CLSC articles fo- cused on WEEE. As the body of literature is growing considerably, this review aims to provide a complete picture of the field, by categorizing the content of the literature and reviewing it into four distinct research types: designing and planning of reverse distribution, decision making and performance evaluation, conceptual framework and qualitative studies. After reviewing the articles, research gaps were identified and a number of future research directions have been identified so that future researchers can work in line with the research gaps in the field. The paper is organized as follows: Section 2 discusses the research metho- dology of the study. Section 3 provides a detailed analysis of the arti- cles. Research gaps are analyzed and future research directions are addressed in Section 4, and Section 5 reaches a conclusion.

2. Research methodology

A literature review plays a critical role in scholarship as well as it helps to explore and structure thoroughly a particular research area (Easterby-Smith et al., 2012; Vom Brocke et al., 2009). With a valid literature review, knowledge on the concerning area can be further advanced by identifying key conceptual contents that works as a path to new theory development and new scope of investigation (Machi and McEvoy, 2016; Meredith, 1993). For a systematic literature review, this study implemented four steps processes as prescribed by Mayring (2001) under the qualitative content analysis method: material collec- tion, descriptive analysis, category selection and finally, material eva- luation. Fig. 3 shows four steps process model for content analysis method. An extensive description of the method can be found in Mayring’s recent publication (Mayring, 2014). The application of the method for reviewing supply chain management literature can be found in papers by Seuring and Gold (2012) and Seuring et al. (2005). Several of the previous review articles (non EOL product focused) in the RL/ CLSC field (e.g. Seuring and Gold (2012), Gold et al. (2010), Govindan et al. (2015), Agrawal et al. (2015)) have implemented this metho- dology.

2.1. Material collection

In this literature review material collection and unit of analysis is the first step. A single journal article/conference paper/book chapter was defined as unit of analysis. In this study, a two-phase process was initiated. In the first phase, keywords such as ‘’reverse logistics’’ and ‘’closed-loop supply chain’’ along with ‘’WEEE or E-waste’’ were used in title, abstract and keywords to carry article search. This keywords were used in the Scopus (www.scopus.com), and Web of Science (WoS) da- tabases with an option that search only the papers those written in English. After analyzing title and abstract, further search of literature were inductively connected with the categorization of RL/CLSC i.e.Ta

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M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

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designing and planning of reverse distribution, decision making and performance evaluation, conceptual framework and qualitative studies (e.g. survey, interview etc.). In this case, some of the essential key words were utilized, for instance, ‘’open-loop network design’’, closed- loop network design’’, ‘’third-party reverse logistics provider’’, ‘’vehicle routing’’, ‘’product recovery’’, ‘’organization and business perspective’’, product return’’ and ‘’reverse logistics processes’’; along with the mandatory search term ‘’reverse logistics’’, ‘’closed-loop supply chain’’ and ‘’WEEE/E-waste’’. Besides, those studies that have considered waste battery and printer cartridges were also included in this study. Total 258 papers were retrieved and all collected papers were taken into consideration for a fast check of relevancy and final content for the literature review. Articles those found most relevant to the above mention categorization were considered for this study. Finally, total 157 papers were selected, reviewed and analyzed in detail. Besides, journal articles, in the final collection 26 conference papers and 3 book chapters are included. The selection of the papers for this state-of-the- art review seems sufficient because of concentration (e.g. RL/CLSC of WEEE) on particular issues.

2.2. Descriptive analysis

To understand the broad range of concepts, motivation, modeling approach to a specific problem, papers were arranged from more than sixty journals. Fig. 2 shows the articles published by numerous outlets. From Fig. 4, it is found that most of the papers were published in re- nowned journals such as International Journal of Production Research, Resources, Conservation and Recycling, Waste Management, International Journal of Production Economics and Production and Operations Manage- ment.

Annual distribution of the number of papers published from the year 1999 to 2017 in both RL and CLSC is shown in Fig. 5. Most of the papers were selected from recent publications. 20 papers out of 157 papers were published before the year 2006, whereas rest of the articles (135) were selected from the year 2006 and afterward. The highest number of papers were published in the year 2010. From this trend, it is clear that the number of published papers is growing considerably in the last few years due to the increasing interest of WEEE centric RL/CLSC analysis.

Fig. 2. Closed-loop supply chain of E-waste.

Fig. 3. Summary of the steps involved in qualitative content analysis citied in Seuring et al. (2005).

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

2.3. Category selection

The main categorization of the content of this study and research framework is presented in the Fig. 6. As mentioned in the material collection section, the literature is classified into 4 major research types/categories. These four categories are (1) Designing and planning of reverse distribution (DPRD); (2) Decision making and performance evaluation; (3) conceptual framework based studies; (4) Qualitative studies. Distribution of research articles for 4 different categories is shown in Fig. 7. DPRD has the highest percentage (55%) of publications

whereas other categories possess less percentage which depicts the necessity for future exploration of these areas under the broad RL/CLSC of WEEE research field.

Open-loop network design (OLND), closed-loop network design (CLND), third-party reverse logistics provider (3PRLP) selection and vehicle routing (VR) related papers fall broadly under the category of DPRD. The highest number of papers (51 papers) were published in the OLND sub-category. Fig. 8 shows the trend of published papers in the DPRD research area. The papers in the main field of research were further sub-categorized into specific issues (that evolved during

Fig. 4. Number of papers published in journals, conferences and book chapters.

Fig. 5. Annual distribution of the published papers (157 papers: 1999-2017).

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

material collection and category selection stages) which are shown in Fig. 9.

2.4. Material evaluation

The last and final stage of the content analysis process is the ma- terial evaluation. Rigor in validity is attained by validation test per- formed by two researchers using the deductive and inductive ap- proaches simultaneously. Reliability of the content was measured by both intra-rater reliability and inter-rater reliability. After material collection, all necessary information extracted from the selected articles were input in spreadsheet software conducted by the researchers by which repetition error by the researchers was minimized. With the

same keywords used to search the articles were utilized in the google scholar database, and two researchers found the similar results in identifying correct articles and coding their content in a spreadsheet application. With this reliability was established. Through searching and cross-checking the publications independently, sufficiency, as well as the validity of the correct content of the collected paper, was ac- cepted.

3. In-depth analyses of the literature

3.1. Analyzing papers on DPRD

The primary concern of DPRD is to design collection and

Fig. 6. Categorization and research framework of the studies.

Fig. 7. Distribution of research articles for different categories.

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

transportation network and vehicle route for EOL product acquisition. This specific task also signifies the planning, functions and logistics capability of stakeholders/actors engaged in the networks and how FSC and RSC could be integrated from CLSC perspective. According to Fleischmann et al. (1997), the performance of a reverse distribution

channel mainly depends on three major issues: 1) actors involved in the reverse distribution channel, 2) locations and functions carried out in the channel and 3) relation between FSC and RSC. As mentioned ear- lier, a considerable number of papers have been published with this issue in four major sub-categories which are 1) Open-loop network

Fig. 8. Number of articles published on DPRD.

Fig. 9. Issues of the main research fields of RL/CLSC of WEEE.

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

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p ro d u ct s,

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fa u lt y p ro d u ct s an

d w ar ra n ty

fr ac ti on

M IL P

C P LE

X E co

n om

ic Si n ga

p or e

C ao

an d Z h an

g (2 0 1 1 )

O p ti m al

fl ow

d is tr ib u ti on

of W E E E

in an

R L n et w or k

M ax

im iz at io n of

to ta l p ro fi t

- E vo

lu ti on

ar y al go

ri th m

– (n on

-d om

in at ed

so rt in g

ge n et ic

al go

ri th m

– N SG

A )

T h e T ec h n iq u e fo r O rd er

of P re fe re n ce

by Si m il ar it y to

Id ea l So

lu ti on

(T O P SI S)

E co

n om

ic an

d E n vi ro n m en

ta l

C h in a

W an

g et

al . (2 0 1 1 )

Fa ci li ty

lo ca ti on

O p ti m u m

n u m be

r of

lo ca ti on

s, ca p ac it ie s of

co ll ec ti on

sp ot s an

d d is p os al

st at io n s at

m in im

u m

d is u ti li ty

of co

n su m er .

- M u lt i- ob

je ct iv e in te ge

r p ro gr am

m in g (I P )

So ci al

an d

E co

n om

ic C h in a

B er ek

et li et

al . (2 0 1 1 )

W E E E tr ea tm

en t st ra te gy

- -

M u lt i- at tr ib u te

gr ou

p d ec is io n

m ak

in g m od

el Fu

zz y li n ea r p ro gr am

m in g

te ch

n iq u e fo r

m u lt id im

en si on

al an

al ys is

of p re fe re n ce

(L IN

M A P )

So ci al

an d

E co

n om

ic T u rk ey

G om

es et

al . (2 0 1 1 )

W E E E re co

ve ry

n et w or k

M in im

iz e to ta l n et w or k co

st -

M IP

an d G en

er al

A lg eb

ra ic

M od

el in g Sy

st em

(G A M S)

C P LE

X E co

n om

ic P or tu ga

(c on

ti nu

ed on

ne xt

pa ge )

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

T ab

le 2 (c on

ti nu

ed )

R ef er en

ce M od

el fo cu

s O bj ec ti ve

fu n ct io n s

U n ce rt ai n ti es / co

n st ra in ts

co n si d er ed

in th e m od

el U ti li ze d m od

el in g ap

p ro ac h

So lv ed

by Su

st ai n ab

il it y

d im

en si on

co n si d er ed

C ou

n tr y

T u zk ay

a et

al . (2 0 1 1 )

M et h od

ol og

ic al

d ev

el op

m en

t of

R L

n et w or k d es ig n

A lt er n at iv e lo ca ti on

s of

ce n tr al iz ed

re tu rn

ce n te r an

d co

st m in im

iz at io n

V ar ia ti on

in ex p ec te d re tu rn

vo lu m e of

W E E E

In te gr at ed

A n al yt ic

n et w or k

p ro ce ss

(A N P ) an

d fu zz y-

T O P SI S

G A

E co

n om

ic T u rk ey

X ia n fe n g et

al . (2 0 1 0 )

D ec is io n su p p or t fo r R L n et w or k

d es ig n

- -

LP LI N G O , Fl ex si m

E co

n om

ic C h in a

Z h i et

al . (2 0 1 0 )

R em

an u fa ct u ri n g- ba

se d R L n et w or k

d es ig n

O p ti m al

lo ca ti on

of co

ll ec ti on

ce n te rs , d is as se m bl y ce n te rs ,

re tu rn in g ce n te rs

an d

id en

ti fi ca ti on

of op

ti m u m

sh ip m en

t p at h .

- M at h em

at ic al

m od

el in g

G A

E co

n om

ic C h in a

C h oi

an d Ft h en

ak is

(2 0 1 0 )

O p er at io n al

m od

el in g of

P h ot ov

ol ta ic

(P V ) re cy cl in g

n et w or k

- -

M at h em

at ic al

m od

el in g

- E co

n om

ic U SA

G am

be ri n i et

al . (2 0 1 0 )

R es ou

rc e al lo ca ti on

an d

en vi ro n m en

ta l im

p ac ts

of W E E E

tr an

sp or ta ti on

n et w or k

N ec es sa ry

w or ki n g d ay

s an

d m in im

u m

n u m be

r of

re qu

ir ed

ve h ic le

fo r op

er at io n

- Li fe cy cl e as se ss m en

t (L C A )

m od

el in g an

d si m u la ti on

Si m aP

ro 6 .0

an d Si m u l8

E co

n om

ic ,

en vi ro n m en

ta l an

d T ec h n ic al

It al y

A ch

il la s et

al . (2 0 1 0 )

O p ti m iz at io n of

co ll ec ti on

p oi n ts

an d re cy cl in g fa ci li ti es

M in im

iz at io n of

co st

- M IL P

C P LE

X E co

n om

ic G re ec e

K aw

a an

d G ol in sk a (2 0 1 0 )

R ec ov

er y n et w or k ar ra n ge

m en

t (R

N A )

- R ec ov

er y ti m e an

d re tu rn

qu an

ti ty

G ra p h th eo

ry an

d ag

en t

te ch

n ol og

y -

E co

n om

ic P ol an

H an

qi n g an

d R u (2 0 0 9 )

R ec ov

er y st at io n lo ca ti on

s fo r

T h ir d -p ar ty

R L se rv ic e p ro vi d er

- -

M at h em

at ic al

m od

el in g

E co

n om

ic C h in a

D en

g an

d Sh

ao (2 0 0 9 )

M u lt i- p ro d u ct

fl ow

-b as ed

re cy cl in g

n et w or k

M in im

iz at io n of

to ta l co

st -

A n al yt ic al

m od

el in g

M A T LA

B E co

n om

ic T ai w an

G u er ra

et al . (2 0 0 9 )

V eh

ic le

an al ys is

– n u m be

r of

ve h ic le s to

be al lo ca te d

M in im

iz at io n of

in te rv en

ti on

ti m e

- Si m u la ti on

A R E N A

E co

n om

ic It al y

G ru n ow

an d G ob

bi (2 0 0 9 )

O p ti m iz at io n of

lo ca ti on

s of

co ll ec ti on

st at io n s in

an R L n et w or k

- -

M IL P

C P LE

X E co

n om

ic D en

m ar k

W an

g et

al . (2 0 0 8 )

O p ti m iz at io n of

th e tr ea tm

en t an

d tr an

sf er

st at io n lo ca ti on

s T ot al

co st , d is ta n ce s an

d W E E E am

ou n t to

be tr an

sf er re d

- Fu

zz y m u lt i- ob

je ct iv e IP

LI N G O

E co

n om

ic C h in a

Sh an

sh an

an d K ej in g

(2 0 0 8 )

O p ti m iz at io n of

W E E E re cy cl in g

n et w or k

C os t m in im

iz at io n

- M IL P

LI N G O

E co

n om

ic C h in a

C ag

n o et

al . (2 0 0 8 )

E va

lu at io n of

th e ca p ac it y an

d co

st of

th e ex is ti n g R L n et w or k

- -

A n al yt ic al

m od

el -

E co

n om

ic It al y

Le e an

d D on

g (2 0 0 8 )

Lo ca ti on

-a ll oc

at io n of

p ro d u ct

re co

ve ry

n et w or k

- -

T w o- st ag

e h eu

ri st ic -

d et er m in is ti c p ro gr am

m in g

C P LE

X E co

n om

ic Si n ga

p or e

Q u ei ru ga

et al . (2 0 0 8 )

P er fo rm

an ce

ev al u at io n of

th e

re cy cl in g p la n t lo ca ti on

s -

- M C D M -P R O M E T H E E

- E co

n om

ic Sp

ai n

R ou

si s et

al . (2 0 0 8 )

D et er m in at io n of

be st

W E E E

m an

ag em

en t sc en

ar io

(i .e

re co

ve ry

lo ca ti on

s, n et w or k d es ig n )

- -

M C D M -P R O M E T H E E

D E C IS IO

N LA

B so ft w ar e

E co

n om

ic , so ci al

an d en

vi ro n m en

ta l

C yp

ru s

Sr iv as ta va

(2 0 0 8 a,

2 0 0 8 b)

C os t- effi

ci en

t lo ca ti on

–a ll oc

at io n of

va lu e re co

ve ry

n et w or k (i .e .

co ll ec ti on

ce n te rs

an d re w or k

fa ci li ti es )

- -

E co

n om

ic In d ia

W an

g an

d Y an

g (2 0 0 7 )

D es ig n in g Lo

ca ti on

an d

co n fi gu

ra ti on

of re cy cl in g n et w or k

M ax

im u m

u ti li za ti on

of re so u rc es

an d m ax

im iz at io n

of re ve

n u e

- M IL P

C P LE

X E co

n om

ic T ai w an

K ar a et

al . (2 0 0 7 )

C os t of

R L n et w or k

- -

Si m u la ti on

A re n a so ft w ar e

E co

n om

ic A u st ra li a

C h an

g et

al . (2 0 0 6 )

Lo ca ti on

se le ct io n in

R L n et w or k

M in im

iz at io n of

th e to ta l co

st -

M IP

LI N G O

E co

n om

ic C h in a

A h lu w al ia

an d N em

a (2 0 0 6 )

M u lt i- ob

je ct iv e op

ti m iz at io n of

R L

n et w or k

M in im

iz in g en

vi ro n m en

ta l

ri sk

an d co

st -

IL P

- E co

n om

ic an

d en

vi ro n m en

ta l

In d ia

(c on

ti nu

ed on

ne xt

pa ge )

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

T ab

le 2 (c on

ti nu

ed )

R ef er en

ce M od

el fo cu

s O bj ec ti ve

fu n ct io n s

U n ce rt ai n ti es / co

n st ra in ts

co n si d er ed

in th e m od

el U ti li ze d m od

el in g ap

p ro ac h

So lv ed

by Su

st ai n ab

il it y

d im

en si on

co n si d er ed

C ou

n tr y

Fr an

ke et

al . (2 0 0 6 )

R em

an u fa ct u ri n g ca p ac it ie s an

d p ro d u ct io n p ro gr am

s op

ti m iz at io n

- Q u al it y,

qu an

ti ty , re li ab

il it y of

ca p ac it ie s,

p ro ce ss in g ti m es ,

d em

an d fo r re m an

u fa ct u re d

p ro d u ct s

IL P

LI N G O

E co

n om

ic G er m an

N ag

u rn ey

an d T oy

as ak

i (2 0 0 5 )

D ev

el op

m en

t of

re cy cl in g p ol ic y

in st ru m en

ts fo r m u lt i- ti er ed

re cy cl in g n et w or k

- -

- FO

R T R A N

ba se d al go

ri th m

- U SA

Sh ih

(2 0 0 1 )

O p ti m iz at io n of

in fr as tr u ct u re

d es ig n an

d re ve

rs e n et w or k fl ow

(C ol le ct io n an

d re co

ve ry

lo ca ti on

s, re so u rc e al lo ca ti on

s, m at er ia l

fl ow

M in im

iz at io n of

to ta l co

st Fi xe d co

st an

d op

er at io n co

st ,

an d re ve

n u e fr om

se ll in g

re cl ai m ed

m at er ia l

M IP

- E co

n om

ic T ai w an

Fl ei sc h m an

n et

al . (2 0 0 1 )

Im p ac t of

p ro d u ct

re co

ve ry

on R L

n et w or k an

d fa ci li ty

lo ca ti on

- -

M IL P

C P LE

X E co

n om

ic T h e N et h er la n d s

So d h i an

d R ei m er

(2 0 0 1 )

R ec yc li n g n et w or k m od

el in g

- -

N on

-l in ea r m at h em

at ic al

p ro gr am

m in g

C P LE

X an

d G en

er al

al ge

br ai c

m od

el in g sy st em

(G A M S)

E co

n om

ic U SA

K ri kk

e et

al . (1 9 9 9 )

P ro d u ct

re co

ve ry

an d

re m an

u fa ct u ri n g

M in im

iz at io n of

to ta l

op er at io n al

co st

- M IL P

- E co

n om

ic T h e N et h er la n d s

N ag

el an

d M ey

er (1 9 9 9 )

A sp ec ts

of R L,

d is as se m bl y an

d re cy cl in g in

E O L n et w or ks

- -

LC A

C om

p u te r p ro gr am

s (F or tr an

, C , C + + ,

Ja va

)

E co

n om

ic an

d en

vi ro n m en

ta l

G er m an

Sh ok

oh ya

r et

al . (2 0 1 3 )

O p ti m iz at io n m od

el fo r ec o- le as in g

O p ti m al

n u m be

r of

le as in g

p er io d s,

op ti m al

d u ra ti on

of le as in g an

d op

ti m al

E O L

op ti on

R ep

ai r an

d re p la ce m en

t se rv ic es

M IP

Si m u la ti on

(A re n a so ft w ar e) ,

O p ti m iz at io n (O

p t Q u es t

so ft w ar e)

E co

n om

ic an

d en

vi ro n m en

ta l

Ir an

A ra bi

et al . (2 0 1 7 )

O p ti m iz at io n of

w ar ra n ty

an d ou

t- of -w

ar ra n ty

p er io d fo r E E E p ro d u ct

C os t m in im

iz at io n

- T h e St ac ke

lb er g ga

m e th eo

ry B ac kw

ar d in d u ct io n m et h od

E co

n om

ic an

d en

vi ro n m en

ta l

Ir an

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

design (OLND), 2) Closed-loop network design (CLND), 3) Analyzing third-party reverse-logistics provider (3PRLP) selection, and 4) Vehicle routing problem (VRP). In the following sub-sections, the papers are reviewed in details.

3.1.1. Open-loop network design (OLND) According to Salema et al. (2007), ‘’An RL network establishes a

relationship between the market that releases used products and the market for “new” products. When these two markets coincide, we talk of a closed-loop network, otherwise of an open loop’’. OLND focuses on the activities and flows of the reverse channel. Collection, inspection, sorting, disassembly, reprocessing/recycling, and disposal operations are the major RL activities, with the flow of returned products from one place/process to another (Akçalı et al., 2009). The selected papers under the heading of OLND in this study are divided into 4 major subcategories that are described in this subsection. The detailed sum- mary of the OLND studies is illustrated in Table 2.

3.1.1.1. Location-allocation problem. Shokouhyar and Aalirezaei (2017) determined the most appropriate locations of collection centers (CCs) and recycling plants (RPs) in a WEEE RL network in Iran using multi- objective genetic algorithms (GA). Important decisions on the trade-off among social, environmental and economic impacts of the network design can be made from this study. Ayvaz et al. (2015) developed a two-stage stochastic programming model that determined optimal locations for collecting, sorting and recycling centers (RCs). Besides finding the locations, it also determined the amount of WEEE (in weight) to be transported between nodes in a generic RL network. Kilic et al. (2015) developed a stochastic mixed-integer linear programming (MILP) model that determined the optimum locations of storage sites and recycling facilities that fulfill the minimum recycling rate prescribed by EU WEEE Directive 2012/19/EU (Directive, 2012).

Shokohyar and Mansour (2013) developed a simulation-based op- timization model to determine the optimal locations for CCs and RPs in a network. This research considered three dimensions of the sustain- ability criteria. Considering a social sustainability indicator, this re- search considered employment, damage to the worker, local develop- ment. Total net profit was considered under an economic sustainability indicator, while the environmental impact was quantified using an Eco- indicator related to WEEE transportation. Gomes et al. (2011) proposed a generic nationwide WEEE recovery network (RN) model to identify the best location of CCs and sorting centers (SCs) with short-term (tactical - less than a year) network planning. Besides economic cost, environmental costs attributed to CO2 emissions may influence network decisions - locations and mode of transport. Tuzkaya et al. (2011) de- veloped a novel methodology for RL network design (RLND) that uti- lized integrated multi-criteria decision making (MCDM) and GA methodology to investigate two strategic-level (long-term) objectives such as the best possible locations for CCs and cost minimization of the RL network.

Xianfeng et al. (2010) proposed a linear-programming (LP) model for the recycling network to identify collection and recovery locations, resource allocation, and material flows of the network. This simulation- based work identified that the uncertainties of the recycling network were time, quantity and quality and recycling levels. Hanqing and Ru (2009) analyzed a model that was concerned with a self-sustaining recovery pattern of a 3PRLP focusing on appropriate recovery locations. Wang et al. (2008) developed a fuzzy multi-objective LP model that optimizes the locations of transfer stations (TSs) and treatment facilities (TFs) considering five objective functions. Achillas et al. (2010) pre- sented a decision support tool for policy makers to optimize the existing infrastructure of collection points and recycling facilities in an RL network in Greece. The authors implemented mixed integer linear programming as their modeling approach which was later solved by CPLEX solver.

Chang et al. (2006) developed a mixed-integer programming (MIP)

model that aimed to optimize the RL network structure and minimize the total cost including the collection cost, fixed costs, transportation cost, daily operation cost, waste disposal cost. Cost minimization was achieved by selecting optimum locations for disassembly/reprocessing plants in the network. Shih (2001) proposed an optimization model for infrastructure design and reverse network flow for home appliances and computers in Taiwan. In the model, the authors considered the total cost (e.g. transportation cost, operating cost, fixed cost for new facil- ities, final disposal cost and landfill cost) minimization in various as- pects of the RL network such as collection and recovery locations, re- source allocations and material flows within the network. Chong et al. (2014) examined an economically self-sustained RL network design considering collection centers, processing centers, transportation, sec- ondary market, recycle centers and disposal sites that can cover the overall expenses of an RL system.

Ayvaz and Bolat (2014) presented a two-stage stochastic RLND model making strategic decisions on RP locations. Wang et al. (2011) developed a multi-echelon RL network for the purpose of collecting and processing WEEE. The authors tried to identify the best possible loca- tions of CCs and disposal stations. Source-specific circulation of WEEE from collection centers to disposal facilities was also identified. Grunow and Gobbi (2009) developed an MILP model to evaluate the config- uration of the existing CC’s locations. The study found that collective schemes (in Danish Producer Responsibility System) are economically beneficial for logistic activities, better-off in developing a competitive market and cost-efficient in providing services.

To achieve better RSC management with flexibility in its design, Wang and Yang (2007) developed an MILP model that integrated fa- cility location and configuration problems of WEEE recycling. Max- imizing the overall utilization of the returned products and revenue generation from recycling were the two major objectives in their RL modeling. Guerra et al. (2009) developed a modular simulation model for the number of vehicles to be assigned in an RL network considering minimization of the intervention time at the collection centers. Ahluwalia and Nema (2006) developed an integrated planning and design model using integer linear programming (ILP) to minimize the environmental risk as well as cost from a computer-waste management system. With the model, they presented a decision support tool that can be used to select an optimum configuration of waste management fa- cilities - segregation, storage, treatment/processing, reuse/recycle and disposal, and allocation of waste in the facilities. Zhi et al. (2010) for- mulated a two-stage resource-constrained project-scheduling problem (RCPSP) based RL network with a remanufacturing focus. Minimizing costs, quantity of WEEE, and return and disassembly centers were the major objects of the modeling. Authors found that RCPSP is beneficial for WEEE take-back logistics when locations of the collecting centers and disassembly centers are uncertain.

3.1.1.2. Product recovery (PR). Qiang and Zhou (2016) developed a robust RL network-optimization model considering uncertainty of recovery on the basis of a risk preference coefficient and a penalty coefficient. Assavapokee and Wongthatsanekorn (2012) created a deterministic strategic infrastructural RLND for the state of Texas in the USA, so that product recovery activities can be supported by the network for old TVs, CPUs and CRT monitors. Golinska and Kawa (2011) proposed a recovery network arrangement (RNA) model with a focus on recycling. The authors solved problems arising in the typical dynamic configuration of an RL network - goods flow visualization, coordination mechanism with FL, minimization of delivery time, stock and cost.

Kawa and Golinska (2010) proposed a model to restructure the configuration of a recycling RN for waste computers in a dynamic supply-chain scenario where recycling enterprises are dependent on each other. Their model provided potential ways in finding cost-effi- cient supply-chain paths of the whole enterprise network, according to their individual appropriate capacities. The leader company in the

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

supply-chain network can provide supply of recycled materials to its customers quickly with competitive price. Cagno et al. (2008) proposed an analytical model for RN to evaluate the capacity and cost of the existing network of refrigerator recycling with an estimation of future values. Lee and Dong (2008) developed a network-flow-based de- terministic programming model for the purpose of designing an end-of- lease computer products RN that consists of both forward and reverse logistics flow.

Srivastava (2008a, 2008b) designed a multi-period value RN of re- turned white goods such as refrigerators, washing machines. He found that for flexible volume acquisition, remanufacturing is not a viable economic proposition for India. Fleischmann et al. (2001) developed a facility location model for PR and remanufacturing by integrating RN with the existing RL structure in the Netherlands. Fixed costs, trans- portation costs, rate of return, recovery processing technology, com- bining FL with reverse transportation, regional legislative requirements and EOL management were considered in the model. Sodhi and Reimer (2001) developed a non-linear mathematical programming model for optimizing recycling operations (i.e. disassembly and material-recovery decisions of recyclers and processors) in such a way that the net cost for material removal becomes a minimum, thus economic sustainability of WEEE recycling can be achieved. Krikke et al. (1999) established a stochastic dynamic-programming (SDP) model to determine an optimal degree of disassembly with optimal recovery and disposal options, so that the recycling cost of PC monitors can be reduced.

Piplani and Saraswat (2012) developed a min-max based robust optimization model using MILP to determine the suitability of facility utilization according to product flow and to address the uncertainties of the repair and refurbishing network, namely as number of products returned, percent of faulty products and warranty fraction of modules.

Xie et al. (2013) proposed a model on an RL reuse network based on the election-campaign algorithm (ECA). Experience from the domestic and overseas research about RL management, the authors provided an optimized model that focus the minimization of disused electronic items at regional level in China. Kara et al. (2007) developed a simu- lation-based RL network model for collecting EOL white goods from the Sydney Metropolitan Area in Australia. With the study, it was under- stood how the collection system interacted with the current WEEE management structure.

3.1.1.3. Cost. Shanshan and Kejing (2008) developed an integrated optimization model for location of the disassembly and bulk recycling facilities in a recycling network. In addition, optimized material flows among different actors in the network were determined, where cost minimization was considered as the objective function. Yu and Solvang (2016) proposed a stochastic optimization model to design and plan an RL system considering economic efficiency and environmental impacts on the system. The model provided policy implications for government authority in allocating subsidies for companies working with WEEE treatment.

Elbadrawy et al. (2015) proposed a mathematical model for an RL recycling network that aimed to minimize the total cost of the network, consisting of collection cost, installation cost of setting up sorting fa- cilities, repairing. Besides, the costs, the model also considered the processing capacity of the recycling facilities and the optimal trans- ported weights of WEEE from collection to recycling facilities. Yu and Solvang (2013) designed an RL network to treat multi-sourced WEEE considering environmental (in the form of greenhouse gas emission from transportation) and economic (cost minimization) dimensions. They found that, even though reuse, repair, remanufacturing and re- cycling of WEEE significantly increases the profit of the network, gov- ernment still needs to provide subsidies and incentives to operators present in the RL network. Cao and Zhang (2011) proposed an in- tegrated method based on multi-objective optimization (NSGA II) and a multi-attribute decision-making model analyzing the optimal flow of WEEE in an RL network considering the total profit and accumulated energy consumption in the network.

Dat et al. (2012) proposed an RL network-optimization model for recycling that aimed to minimize the total processing cost of the net- work. They found that, in order to reduce the total cost, the transpor- tation cost should be minimized. Achillas et al. (2012) presented a single-period multi-criteria optimization model for multi-type carriers of WEEE to allocate the types of carrier to be used in an RL network. Total logistics costs, consumption of fossil fuel and production of emissions due to transportation were estimated by the model. Deng and Shao (2009) proposed an analytical recycling network configuration model to find the total minimum cost (transportation cost, operating cost and final disposal cost) in the presence of a recycling capacity constraint of the network, and sales revenue of reclaimed materials derived from the network. The authors found that WEEE compression at pre-processing sites is an important task for the entire recycling process and provided the essential implication of product design for recycling.

3.1.1.4. Secondary market. Rousis et al. (2008) developed a decision- making model based on the MCDM method using PROMETHEE to investigate possible alternative scenarios for WEEE management in Cyprus. According to the developed model, partial disassembling of WEEE and forwarding the recyclable material fractions to secondary markets and disposing of the residues to landfills was the best scenario in the existing setting. Franke et al. (2006) developed a generic mobile- phone remanufacturing plant’s capacity planning and facility adoption planning by using a discrete-event capacity and program planning simulation model. In the model, they considered uncertainties in the remanufacturing process, such as the quantity and condition of mobile phones, reliability of capacities, processing times, and demand for remanufactured product.

Nagel and Meyer (1999) proposed a new approach that system- atically analyzed and modeled EOL networks, focusing on disassembly and recycling of refrigerators in Germany from ecological and eco- nomical points of view. Bereketli et al. (2011) developed a fuzzy linear- programming technique for multi-dimensional analysis of a preference (LINMAP) model to evaluate and select the best WEEE treatment strategy in an RL network. It was found that reuse and recycling were the best strategy in the current management practice in Turkey.

Choi and Fthenakis (2010) developed an operational mathematical model to assess the feasibility of developing a recycling infrastructure for thin-film solar photovoltaic (PV) waste. The authors intended to propose a generalized framework to overcome the challenges in PV waste recycling experiencing mathematical models proposed for other waste products. Nagurney and Toyasaki (2005) presented a multi-tiered network equilibrium model that focused on a policy instrument for recycling. They found that policy instruments that involve original equipment manufacturers (OEMs) and integrate a classic supply-chain network with recycling perform best in terms of efficiency and effec- tiveness, as seen in Japan and in European member states. Liu et al. (2014) developed an evolutionary RL network model that measured the enterprise’s logistics capability standard as an effective output of the network. In this study, authors formulated the problem in multiple objective programming using LINDO6.1. The results showed that maximum utilization of processing centers in the network had impact on lower operating cost and maximum profit for recycled products prepared for secondary market.

3.1.1.5. After-sales service. Due to increasing customer awareness and EPR policy, manufacturers are now responsible for product servicing after selling their equipment to ensure better economic and environmental performance. Besides the traditional purchase of EEE, leasing and offering product warranty became popular means of minimizing waste generation as well as prolongation of EOL phase of the EEE (Mont, 2000; Shokohyar et al., 2014).

Shokohyar et al. (2013) presented an integrated MIP and simula- tion-based optimization model to determine the optimal number of leasing periods, the optimal duration of leasing period and optimal EOL

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

options for the diverse range of components of returned product con- sidering uncertainties of repair and replacement services performed at the time of leasing periods. The results of the study provide optimal solutions to EEE leasing companies achieving maximum profit, minimum environmental impact and selecting best EOL option.

Considering producer and customer’s viewpoint towards after sales service, Arabi et al. (2017) developed a Stackelberg game theory finding optimized periods of warranty (for in-use products) and out-of- warranty (EOL products) that minimize total cost incurred during the periods. The authors implemented backward induction method solving the problem and results of the study showed that offering warranty period not only optimizes the cost of the producer and consumer but also helps to extend the product lifecycle towards achieving environ- mental objectives.

3.1.2. Closed-loop network design (CLND) Network design with CLSC refers to transforming a supply chain

into a closed-loop entity by forming a direct and coordinated re- lationship between FL activities (i.e. material processing, manu- facturing, and distribution) and tasks associated with RSC (Akçalı et al., 2009). Compared to OLND, only a few studies have been found that considered a CLSC network focusing on WEEE; they are discussed in this part of the paper. A summary of the CLND studies are presented in the Table 3.

3.1.2.1. Location-allocation problem. Chen et al. (2015) developed a CLND in which the delivery routes and quantity of different materials derived from printer cartridges were considered, for achieving a maximum recycling rate and profit. Their model provided near- optimal and time-efficient solutions for optimization of the CLSC network. Amin and Zhang (2013) proposed a multi-objective three- stage CLSC model to evaluate and select three major factors in a network that determine the configuration of the network: suppliers of used products, remanufacturing subcontractors, and refurbishing sites.

Qiang et al. (2013) investigated a CLSC network in the USA, con- sidering competition, distribution-channel investment, and un- certainties in the recycling network (i.e. yield rate and demand) for printer cartridges. In their model, they considered three decentralized decision makers – raw-material suppliers, manufacturers (they collect recycled products directly from the demand market), and retail outlets. Alumur et al. (2012) proposed a multi-period profit maximization CLSC model aiming to improve the network configuration and capacities of inspection centers and remanufacturing plants by optimizing locations. The model made an impact on reducing transportation costs between facilities.

Amin and Zhang (2012) proposed an MILP model based on re- turn–recovery pairs and PLC to configure a CLSC network that consisted of manufacturer, collection, repair, disassembly, recycling, and disposal sites for waste mobile phones in Canada. Krikke (2011) proposed a CLSC network-configuration model with combined disposition and lo- cation-transport decisions to assess the impact of photocopier machine recovery and remanufacturing on carbon foot printing. The author found that a regional CLSC network could perform efficiently and ef- fectively when recycling is included.

Easwaran and Üster (2010) presented a multi-product CLND model that considered hybrid manufacturing/remanufacturing facilities and finite-capacity hybrid distribution/collection centers to serve a set of retail locations. Chandiran and Surya Prakasa Rao (2008) investigated a centralized CLSC network-design model that had facility location and network configuration for distribution and collection of spent batteries. Decentralized network, manufacturer's dilemma in managerial control over the collection, disturbance to existing network, time pressure and integral design of both reverse and forward supply chain flow were addressed in the study.

Schultmann et al. (2003) developed a hybrid CLSC planning and optimization model that deals with location-specific recycling options

for spent batteries in the steelmaking industry. They found that the performance of recycling can be improved by modifying the recovery strategies of a network. Jayaraman et al. (1999) proposed a re- manufacturing-focused CLSC model that focused on the location of re- manufacturing/distribution facilities, the trans-shipment, production, and stocking of the optimal quantities of remanufactured products, and managerial decisions.

Gupta and Evans (2009) developed a multi-product multi-objective goal-programming (GP) model that analyzed the operational level of a CLSC using three different techniques - why–what's stopping analysis, fundamental objective hierarchy, and means objective network.

3.1.2.2. Cost. Kannan et al. (2010) developed a mathematical model using MILP considering a multi-echelon, multi-period, multi-product CLSC network with a focus on cost reduction, for making decisions in the material procurement, distribution, recycling and disposal of waste batteries. Fernandes et al. (2010) constructed a CLSC network- optimization model of spent lead batteries considering production of the batteries, their distribution to customers, and EOL collection in Portugal. The costs included in their modeling were cost of opening warehouses, raw materials acquisition from supplier, EOL product acquisition from customers, and transportation resources. Grant and Banomyong (2010) investigated product-recovery-management related activities that affected the strategic design and implementation of a CLSC for single‐use cameras. They found that OEMs could benefit from the entire supply chain by standardizing high‐quality raw materials, using a modular product structure, maintaining control over cost of the entire process and avoiding third‐party collectors and processors.

Chouinard et al. (2008) proposed a stochastic programming model to design a CLSC network considering location specific network-design decisions such as recovery and demand volumes with respect to capa- city constraints and operating costs. Hammond and Beullens (2007) presented a variational inequality approach to strategic modeling of oligopolistic CLSC considering legislation. The authors suggested that reverse-chain activities could be stimulated by legislation when some minimum recovery levels of all new products were included. On con- trary, when there is interdependence of a number of factors: increase in collection targets, landfill costs and manufacturer-pay schemes, legis- lation became difficult to implement.

According to Mata-Lima et al. (2013) the dimensions of the sus- tainability triangle comprise social, economic and environmental as- pects linked with technology. Considering these dimensions, papers on both OLND and CLND were analyzed for which dimension they cov- ered. Fig. 10 shows the coverage of sustainability dimensions in the network-design studies. It was found that the economic dimension was given the highest priority in designing the networks, whereas social and environmental issues are poorly addressed. Only three studies were found that considered economic, social and environmental dimensions all together.

Another important aspect in network design is the consideration of uncertainty. Fig. 11 shows the percentage of different uncertainty parameters considered in the network-design studies. The returned amount (28%) was found to be one of the most used uncertainty parameters in designing networks whereas environmental influence, source and reliability of capacities were considered relatively less (only 3%).

3.1.3. Analyzing third-party reverse-logistics provider (3PRLP) selection The concept of 3PRLP was introduced after the successful experi-

ence from third-party logistics (3 PL) in the forward supply chain (Mahmoudzadeh et al., 2013). Krumwiede and Sheu (2002) studied flexibility of transportation in RL activities. It showed that 3PRLP plays a significant role by taking back obsolete items from customers/end- users in implementing EPR principles. In this study, out of the 157 papers (in the main research areas), only 11 papers focused on the 3PRLP problem; they are discussed in this subsection of the paper.

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

T ab

le 3

Su m m ar y of

W E E E /E

-w as te

cl os ed

-l oo

p n et w or k- d es ig n st u d ie s

R ef er en

ce M od

el fo cu

s O bj ec ti ve

fu n ct io n s

U n ce rt ai n ti es /c on

st ra in ts

co n si d er ed

in th e m od

el U ti li ze d m od

el in g

ap p ro ac h

So lv ed

by Su

st ai n ab

il it y

d im

en si on

co n si d er ed

C ou

n tr y

C h en

et al . (2 0 1 5 )

Lo ca ti on

al lo ca ti on

an d

p ro d u ct

re cy cl in g

M ax

im iz at io n of

re cy cl in g ra te

an d

p ro fi ts

- IP

G A

E co

n om

ic C h in a

A m in

an d Z h an

g (2 0 1 3 )

N et w or k co

n fi gu

ra ti on

M in im

iz at io n of

co st s an

d d ef ec t ra te s,

an d m ax

im iz at io n of

w ei gh

ts , an

d on

- ti m e d el iv er y

Su p p li er

se le ct io n p ro ce ss

an d d em

an d

M u lt i- ob

je ct iv e M IL P

G A M S,

se n si ti vi ty

an al ys is , fu zz y

se ts

th eo

ry E co

n om

ic C an

ad a

Q ia n g et

al . (2 0 1 3 )

N et w or k d es ig n

- D em

an d an

d p ri ce s

M at h em

at ic al

m od

el in g

C on

ti n u ou

s an

d co

n ve

x al go

ri th m

E co

n om

ic U SA

A lu m u r et

al . (2 0 1 2 )

N et w or k d es ig n

P ro fi t m ax

im iz at io n

- M IL P

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n om

ic G er m an

y A m in

an d Z h an

g (2 0 1 2 )

N et w or k op

ti m iz at io n

P ro fi t m ax

im iz at io n

- M IL P

G A M S,

se n si ti vi ty

an al ys is

E co

n om

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ad a

K ri kk

e (2 0 1 1 )

N et w or k op

ti m iz at io n

- -

- St re am

li n e LC

A So

ci al

an d

en vi ro n m en

ta l

T h e N et h er la n d s

E as w ar an

an d Ü st er

(2 0 1 0 )

N et w or k d es ig n

M in im

iz at io n of

co st

an d op

ti m u m

fa ci li ty

lo ca ti on

- M IL P

C P LE

X an

d B en

d er s' d ec om

p os it io n

te ch

n iq u e

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n om

ic U SA

K an

n an

et al . (2 0 1 0 )

N et w or k d es ig n w it h

re cy cl in g fo cu

s M in im

iz in g to ta l co

st -

M IL P

G A

an d G A M S

E co

n om

ic In d ia

Fe rn an

d es

et al . (2 0 1 0 )

N et w or k d es ig n an

d op

ti m iz at io n

- -

M IL P

- E co

n om

ic P or tu ga

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d B an

om yo

n g

(2 0 1 0 )

P ro d u ct

re co

ve ry

m an

ag em

en t

- -

E co

n om

ic U K

G u p ta

an d E va

n s (2 0 0 9 )

O p er at io n al

d es ig n of

C LS

C n et w or k

M in im

iz at io n of

co st

an d m ax

im iz at io n

of p ro fi ts

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-p re em

p ti ve

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LI N G O

E co

n om

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C h an

d ir an

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ry a

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sa R ao

(2 0 0 8 )

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- E co

n om

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er y n et w or k d es ig n

M in im

iz in g co

st -

St oc

h as ti c

p ro gr am

m in g

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te C ar lo

sa m p li n g m et h od

s, Sa

m p le

av er ag

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p ro xi m at io n

(S A A ), C P LE

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n om

ic C an

ad a

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m on

d an

d B eu

ll en

s (2 0 0 7 )

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n et w or k

- -

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- LP

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n om

ic an

d so ci al

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n et

al . (2 0 0 3 )

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ti m iz at io n

- -

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- E co

n om

ic G er m an

y Ja ya

ra m an

et al . (1 9 9 9 )

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an u fa ct u ri n g- fo cu

se d

n et w or k d es ig n

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th e to ta l co

st -

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se n si ti vi ty

an al ys is

E co

n om

ic U SA

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

Sabtu et al. (2015) presented a study to find influential attributes for selecting and evaluating 3PRLP. They found that the organization role was the most significant attribute that intensified the third party logistic provider’s performance. Xuping et al. (2013) investigated the re- lationship between production enterprises and 3PRLP. They found that 3PRLP’s environmental protection ability and effort level towards working with asymmetric information under the constraints determines

the financial incentive for recycling. Atasu et al. (2013) developed a mathematical model to investigate the impact of the collection cost structure on the optimal reverse-channel choice of manufacturers who have the ability to shape the sales of retailers, and collection quantity (in the case that manufacturers remanufacture their own products).

Wei and Zhao (2013) investigated the decisions of reverse-channel choice in a fuzzy CLSC environment where a manufacturer, a retailer,

Fig. 10. Sustainability dimensions considered in open-loop and closed-loop network-design studies.

Fig. 11. Uncertainty parameters in RL/CLSC WEEE network designs.

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

and a third party collect used products for profit in three different collection modes. The authors considered the demand, manufacturing cost and collecting cost are fuzzy rather than stochastic or determi- nistic. Hong and Yeh (2012) developed a retailer-non retailer collection model for profit maximization. In the retailer-based collection model, a manufacturer cooperated with a third-party to collect the used product from customers, and in a non-retailer case, a third-party company is commissioned by the manufacturer for collection activities. The re- search found that when the return rate, manufacturer’s profits, and channel members’ total profit were considered, non-retailer based col- lection performs better than the other. However, if the third-party firm is a not-for-profit organization working for recycling and disposal, then retailer-based collection outperforms.

Sasikumar and Haq (2011) designed an optimized multi-echelon, multi-product closed-loop distribution supply chain (CLDSC) network integrating the issue of selecting the best 3PRLP in order to achieve efficiency in cost and an optimum delivery schedule. Results of the study showed that cost reduction from CLDSC could be achieved by optimizing the cost of the forward-distribution channel. Cheng and Lee (2010) developed a decision-making approach for practitioners of RL in industrial marketing on outsourcing of 3PRL for the thin-film-transistor liquid-crystal display (TFT-LCD) sector in Taiwan. The authors found that information technology (IT) management is an essential activity in outsourcing (in terms of accommodating return) and this task can be performed better by 3PRLP than the manufacturers of TFT-LCDs. Kannan (2009) proposed a structured model for evaluating and se- lecting the best 3PRLP under a fuzzy environment for the battery in- dustry by formulating the problem as MCDM which was solved by the AHP and fuzzy analytic hierarchy process (FAHP).

Yuksel (2009) developed a WEEE collection-center location model for 3PRLP considering three factors - cost, accessibility and environ- ment using the AHP method. The model evaluated the existing locations of the centers in Turkey then compared with the best alternatives. Xanthopoulos and Iakovou (2009) proposed a methodology that aimed to integrate optimal designing of disassembly processes and aggregate planning of the recovery processes for WEEE. In the study, a simulation was implemented for capturing uncertainties in RL operations. The overall objective of the methodology was to recover both ecological and economic value from the recovered WEEE items, and thus reduce the produced quantities of WEEE. This methodology provided effective decision support to mid-level management involved in resource re- covery. Xu (2008) introduced a WEEE take-back information platform based on the Electronic Product Code (EPC) that allowed involvement of various agents in the RSC for information sharing and to measure the responsibility and efficiency of the 3PRLPs in the take-back system.

3.1.4. Vehicle routing problem (VRP) Based on combinatorial optimization and IP, the vehicle routing

problem (VRP) typically seeks the optimum set of routes in a network for vehicle fleets delivering goods or services to a given set of customers at minimum cost (Dantzig and Ramser, 1959). In the conventional FSC, vast number of papers were published, however, in the RL/CLSC lit- erature, this topic should be considered as new. In this subsection the papers are summarized.

Mar-Ortiz et al. (2013) designed a Greedy Randomized Adaptive Searching Procedure (GRASP) algorithm to determine the collection ca- pacity and processing time of a fixed and heterogeneous fleet of vehicles with special features that were generally used in the collection of WEEE from customers. Mar-Ortiz et al. (2012) developed an algorithm to opti- mize emerging waste-white-goods collection systems with three different manufacturing interfaces: network design, vehicle routing and cellular disassembly. Mar-Ortiz et al. (2011) proposed a facility-location oriented collection vehicle routing model to evaluate the overall performance of collection routes and to optimize a recovery network (RN) in Spain. The authors redesigned the recovery network and reduced the number of ve- hicles and the depot size required in the collection route.

Gamberini et al. (2010) presented a WEEE transportation-optimi- zation network model that considered both technical (in terms of sa- turation of vehicle capacity, the utilization of vehicle working times) and environmental performance. Manzini et al., 2011 proposed a model that integrated VR and the allocation of customer demand (according to suppliers) under various modes of transportation. Both cost and en- vironmental effects minimization were considered in the model that supported decision making in transport planning. Gallo et al. (2010) proposed a methodology to analyze the processing time at collection centers to treatment centers combining VR. The research identified efficiency parameters in waste recovery from the customer at the col- lection center and reprocessing center, for recycling that quantifies the current trend of WEEE flows. Guerra et al. (2009) described a logical model of VRP that analyzed the WEEE distribution flow that consisted of the number of vehicles allocated within a region in Italy and the minimum intervention time required at the collection centers. The re- search explored different network configurations and scenarios without imposing high costs, which was achieved by information on the number of vehicles to be adopted in the network.

Kim et al. (2009) presented a VR model in order to minimize the transport distance from WEEE CCs (of local authorities) and distribu- tion centers of major manufacturers to four regional recycling centers located in Korea. Fernández et al. (2006) presented a recycling-focused RSC model concerned with the optimum amount of waste mobile phones to be collected to guarantee the supply of waste for recycling companies. In this VR problem, they considered: 1) the locations of the central and transfer stations, 2) the limited capacity in the VR and 3) the presence of multiple depots in the network. They found that in long- term planning, if a centralized recycling facility is considered in a network it will not be profitable.

3.2. Analyzing the decision-making and performance-evaluation studies

A vast area of research in the RL/CLSC of WEEE focuses on decision making and performance evaluation of the RL/CLSC processes (see Fig. 1) and networks (including transportation), the economic and en- vironmental performance of organizations and businesses and WEEE management. Product acquisition, collection, inspection and sorting, and disposition (i.e. recycling, reuse, repair, remanufacturing and dis- posal) are the major RL/CLSC processes (Agrawal et al., 2015). The papers that considered the issue are given in detail in following sub- sections. The papers are summarized in Table 4.

3.2.1. RL/CLSC process perspectives Tari and Alumur (2014) incepted a multi-period multi-objective

mixed-integer programming decision making model for distributing collected WEEE from collection centers to recycling firms, with same amount. The three objectives were considered: cost minimization, equity among different firms, providing steady flow of products to each firm. Additional focus were given on collection center location and capacities of the collection centers in a given planning horizon. Temur et al. (2014) developed an evaluation criteria to measure performance of a RL system, considering social acceptability, environmental risks, biodiversity conservation, operation and investment costs, energy and transportation infrastructure, legal/political environment, and growth potentials.

Moussiopoulos et al. (2012) proposed a model for evaluating the justification of the present facility locations with future alternatives for WEEE collection in Greece. They also estimated transportation costs by considering national and local waste management conditions, practices and possibilities. They found that WEEE management system can only be profitable when the quantity recovered is maximum. Ponce-Cueto et al. (2011) developed a model using AHP to make decision on col- lection center locations for waste batteries. Calculating the distance between collection points for the model was solved by Visual Basic computer programming. Theoretically, with the model maximum

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

T ab

le 4

Su m m ar y of

d ec is io n -m

ak in g an

d p er fo rm

an ce -e va

lu at io n st u d ie s on

R L/

C LS

C – W E E E /E

-w as te

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ce M od

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fu n ct io n s/ fa ct or s

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co n st ra in ts

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m od

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st ai n ab

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R L fa ci li ty

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an d

re u se

- -

LP G A M S

E co

n om

ic U SA

Fe rr er

an d K et ze n be

rg (2 0 0 4 )

V al u e of

in fo rm

at io n in

re m an

u fa ct u ri n g

Le ad

ti m e an

d re m an

u fa ct u ri n g

co st

P er fo rm

an ce

of re co

ve ry

p ro ce ss

M ar ko

v ch

ai n d ec is io n p ro ce ss

(s to ch

as ti c d yn

am ic

p ro gr am

m in g)

M et h od

of G au

ss ia n el im

in at io n

E co

n om

ic U SA

K ri kk

e et

al . (2 0 0 3 )

O p ti m iz at io n of

n et w or k d es ig n w it h

re p ai ri n g fo cu

s Lo

ca ti on

al lo ca ti on

an d p ro d u ct

d es ig n

R at e of

re tu rn ,

re co

ve ry

fe as ib il it y

an d

re co

ve ry

ta rg et s

M IL P

C P LE

X E co

n om

ic an

d en

vi ro n m en

ta l

T h e N et h er la n d s

R av

i et

al . (2 0 0 5 a,

2 0 0 5 b)

R L im

p le m en

ta ti on

P ro d u ct iv it y im

p ro ve

m en

t -

In te rp re ti ve

st ru ct u ra l

m od

el in g (I SM

) M at ri ce d ’ Im

p ac ts

C ro is e' s

M u lt ip li ca ti on

A p p li qu

ée a U N

C la ss em

en t (M

IC M A C )

E co

n om

ic , so ci al

an d

en vi ro n m en

ta l

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Li u et

al . (2 0 1 0 )

E va

lu at io n of

th e R L ca p ab

il it ie s

Fl ex ib il it y,

op en

n es s an

d ex te n si bi li ty

- M u lt i- st ep

fu zz y an

al yt ic al

m et h od

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se t of

ev al u at io n

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et al . (2 0 1 2 )

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se d fo re ca st in g m od

el P ro d u ct

re tu rn

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d se n si ti vi ty

an al ys is

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n om

ic an

d so ci al

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an ia n et

al .

(2 0 1 3 )

M an

u fa ct u re r’ s co

m p on

en t co

m m on

al it y

d ec is io n

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an u fa ct u ri n g p ro fi ts

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at ic al

m od

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n om

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es an

d N ik ol ai d is

(2 0 1 2 )

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an u fa ct u ri n g- fo cu

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ve ry

op ti m iz at io n

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re tu rn

an d p ro fi ta bi li ty

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LI N G O

E co

n om

ic G re ec e

Li et

al . (2 0 0 9 )

O p ti m u m

p ri ci n g of

re m an

u fa ct u re d

p ro d u ct s

P ro fi t op

ti m iz at io n

- T w o- st ep

st oc

h as ti c d yn

am ic

p ro gr am

m in g

A n al yt ic al

so lu ti on

m et h od

ol og

y E co

n om

ic C h in a

G al br et h an

d B la ck bu

rn (2 0 1 0 )

R em

an u fa ct u ri n g- fo cu

se d m u lt i-

co m m od

it y n et w or k fl ow

m od

el P ro fi t m ax

im iz at io n

- M IN

LP M at h em

at ic a 7 .0

E co

n om

ic U SA

K eh

et al . (2 0 1 2 )

P er fo rm

an ce

ev al u at io n of

in te gr at ed

R L

n et w or k

- -

C as e st u d y

- E co

n om

ic , so ci al

an d

en vi ro n m en

ta l

Fr an

M as le n n ik ov

a an

d Fo

le y (2 0 0 0 )

X er ox

’s p er fo rm

an ce

m ea su re m en

t P ro d u ct

re co

ve ry

an d p ro d u ct iv it y

im p ro ve

m en

t -

C as e st u d y

- E co

n om

ic U K

Li n to n an

d Jo

h n st on

(2 0 0 0 )

IT -b as ed

R L d ec is io n su p p or t sy st em

- -

A lg eb

ra ic

eq u at io n

Si m u la ti on

, Se

n si ti vi ty

an al ys is

E co

n om

ic U SA

Sh ar m a et

al . (2 0 0 7 )

R L an

d as se t m an

ag em

en t d ec is io n s

P ro fi t m ax

im iz at io n

- M u lt i- p er io d M IL P

X p re ss -M

P E co

n om

ic an

d en

vi ro n m en

ta l

U SA

(c on

ti nu

ed on

ne xt

pa ge )

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

T ab

le 4 (c on

ti nu

ed )

R ef er en

ce M od

el fo cu

s O bj ec ti ve

fu n ct io n s/ fa ct or s

U n ce rt ai n ti es /

co n st ra in ts

co n si d er ed

in th e

m od

U ti li ze d m od

el in g ap

p ro ac h

So lv ed

by Su

st ai n ab

il it y

d im

en si on

co n si d er ed

C ou

n tr y

P ot te r et

al . (2 0 1 1 )

C lo se d -l oo

p d is tr ib u ti on

su p p ly

ch ai n

n et w or k d es ig n an

d op

ti m iz at io n

M in im

iz at io n of

co st

an d

m ax

im iz at io n of

re tu rn

ra te

- Fu

zz y m u lt i- cr it er ia

d ec is io n -

m ak

in g (F M C D M ) an

d M IL P

LI N G O

E co

n om

ic U K

D h ib

et al . (2 0 1 6 )

Su st ai n ab

le p er fo rm

an ce

in W E E E

m an

ag em

en t

C om

p ro m is e am

on g th e ac to rs

- -

- E co

n om

ic , so ci al

an d

en vi ro n m en

ta l

T u n is ia

G u id e an

d P en

ti co

(2 0 0 3 )

R em

an u fa ct u ri n g- fo cu

se d fi n an

ci al -

in ce n ti ve

s ba

se d p ro d u ct

re tu rn

P ro fi t m ax

im iz at io n

- C lo se d -l oo

p h ie ra rc h ic al

p la n n in g m od

el -

E co

n om

ic U SA

W ee

K w an

T an

et al .

(2 0 0 3 )

IT -b as ed

off -s h or e p ro d u ct

re tu rn

op ti m iz at io n

- -

E co

n om

ic Si n ga

p or e

G u id e et

al . (2 0 0 8 )

R em

an u fa ct u ri n g d ec is io n s on

re tu rn ed

p ro d u ct s

P ro fi t m ax

im iz at io n an

d fa ci li ty

u ti li za ti on

- A n al yt ic al

m od

el in g

- E co

n om

ic U SA

Ja n se

et al . (2 0 1 0 )

Im p ac t of

R L p ra ct ic e in

bu si n es s

p er fo rm

an ce

Fl ow

of re tu rn ed

p ro d u ct s an

d ti m e

- D ia gn

os ti c m od

el -

E co

n om

ic an

d so ci al

T h e N et h er la n d s

M u kh

op ad

h ya

y an

d M a (2 0 0 9 )

O p ti m iz at io n of

p ro cu

re m en

t an

d p ro d u ct io n d ec is io n s

O p ti m al

ac qu

is it io n of

re tu rn ed

p ro d u ct s an

d or d er

of n ew

p ro d u ct s in

C LS

Q u al it y an

d d em

an d

T w o- st ag

e st oc

h as ti c

p ro gr am

m in g

E co

n om

ic R ep

u bl ic

of So

u th

K or ea

T an

an d K u m ar

(2 0 0 8 )

D ev

el op

m en

t of

d ec is io n m ak

in g m od

el P ro fi t m ax

im iz at io n

- LP

- E co

n om

ic Si n ga

p or e

W ee

K w an

T an

an d

K u m ar

(2 0 0 6 )

D ev

el op

m en

t of

d ec is io n m ak

in g m od

el P ro fi t m ax

im iz at io n

- Sy

st em

d yn

am ic , si m u la ti on

V en

si m

E co

n om

ic Si n ga

p or e

R av

i et

al . (2 0 0 8 )

M an

ag er ia ld

ec is io n m ak

in g in

R L p ro je ct

se le ct io n

- -

M C D M

A N P an

d ze ro

on e go

al p ro gr am

m in g (Z O G P )

In d ia

M az h ar

et al . (2 0 0 7 )

Li fe cy cl e es ti m at io n fo r p ro d u ct

re u se

- -

Li fe

cy cl e d at a an

al ys is ,

W ei bu

ll an

d ar ti fi ci al

n eu

ra l

n et w or ks

- -

A u st ra li a

C h u n g et

al . (2 0 1 4 )

M od

u la r p ro d u ct

d es ig n fo r th e li fe

cy cl e

(D FL

C )

- -

A rc h it ec tu re

an d su p p ly

ch ai n

ev al u at io n m et h od

(A SC

E M ),

M od

u la r D es ig n A p p ro ac h

- E co

n om

ic an

d en

vi ro n m en

ta l

T ai w an

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

number of batteries can be collected from the network in Spain. Machado et al. (2010) developed an assignment model to evaluate

the minimum transportation cost considering distances from sorting centers to treatment and recovery centers, and flow of WEEE within the centers. Result of the study found that utilization of the model effec- tively increase the performance of WEEE management than the existing RL structure in Portugal. Tonanont et al. (2008) developed a mea- surement framework analyzing the performance of reverse channels considering four perspectives - customer satisfaction, sorting and storing, asset recovery and transportation. They used Balanced Scor- ecard (BSC), AHP and Data Envelopment Analysis (DEA).

Wadhwa et al. (2009) developed a group decision support tool by using MCDM and fuzzy-set theory to rank reprocessing alternatives to design effective and efficient flexible return policy considering cost, time, legislative factors, environmental impact, quality and secondary market. The authors ranked the alternatives as Reselling > Repairing > Cannibalization > Remanufacturing > Refurbishing. Zhang et al. (2004) developed a conceptual web based information system that was able to provide decision support for reverse manu- facturing and product environmental impact assessment; and evaluate operations for recycling and remanufacturing process.

Jayaraman (2006) presented an analytical approach to production planning and control for CLSC, focusing on product recovery and reuse. The model attempts to develop formal systems in an intermediate to long-term planning environment that concerned remanufacturing ag- gregate production planning, inventory control, and other tactical de- cision-making. Ferrer and Ketzenberg (2004) developed a SDP using Markov chain decision process to evaluate the value of information (remanufacturing cost, process capabilities, facility performance) on remanufacturing of products such as copier machines and medical equipment.

Krikke et al. (2003) developed a quantitative decision model using MILP for a CLSC design problem for refrigerators to identify optimal locations (centralized vs decentralized) of the repair network, product design for optimization of environmental impact and total cost asso- ciated in the repair network. Ravi et al. (2005a, 2005b) proposed a holistic framework based on ANP approach for selecting alternatives for RL operations for EOL computers. In the model, the authors presented determinants, dimensions, and enablers of the RL with alternatives in a hierarchical form. Considering the dimensions, four perspectives were derived by BSC analysis: customer, internal business, innovation and learning, and finance.

3.2.2. Organizational and business perspectives Liu et al. (2010) developed a WEEE RL performance evaluation

model based on multi-step fuzzy analytical method to observe the im- pact of flexibility, openness and extensibility on RL capability at orga- nization level. Shih et al. (2012) presented a forecasting model that applied ANP process and sensitivity analysis to predict the sales volume of printers in Taiwan, so that recycling and treatment fees as incentives attained from the government can be adjusted for recycling industries. The authors found percentage error that evolved using ANP was small when it was compared with other statistical techniques. Subramanian et al. (2013) investigated manufacturer’s component commonality de- cision of remanufacturing in manufacturing and sales of new products in a CLSC environment.

Nenes and Nikolaidis (2012) developed a multi-period MILP model to manage used mobile phones return from the remanufacturing com- pany’s perspective by incorporating multiple suppliers and several quality levels of returned items for environmentally friendly and eco- nomically viable reuse activities in Greece, under CLSC aspect. Li et al. (2009) developed a two-step SDP optimization model to access the optimal collection price of used-products considering risk attitude of remanufacturer, and to estimate optimal selling price for quantity of remanufactured products as profit of a remanufacturing enterprise in China. Galbreth and Blackburn (2010) developed a mixed integer non-

linear programming (MINLP) model considering multi-commodity network flow with economies of scale and product obsolescence when off-shore remanufacturing was considered (in case of an electronic manufacturing company).

Keh et al. (2012) investigated the performance of IBM Montpellier on three main objectives: 1) economic opportunities via reselling and reusing of parts and components, 2) dealing the issues of waste man- agement and legislation compliance and 3) meeting social challenge by preserving local jobs. All three dimensions of sustainability were highlighted in the research from large multi-national company’s per- spective under the case of CLSC. Maslennikova and Foley (2000) in- vestigated the performance and productivity of Xerox in three areas - environmental performance, customer satisfaction, and improved company performance. The authors found that by incorporating design- for-the-environment (DfE) principles into company’s strategic en- vironmental goal, it reduce resource and energy consumption from factories as well as boost revenue generation. The principles also pushes product redesigning that eventually enhanced PR. Linton and Johnston (2000) developed a decision support system (DSS) for Nortel Networks in order to improve its remanufacturing operations for circuit assem- blies. The modeling of the system consisted of algebraic equation and simulation that offered an integration of RL operation with information technology to better plan outbound and inbound product flows.

Sharma et al. (2007) developed a MILP model from the perspective of an electronic equipment leasing company to assist better leasing, logistics and asset management decisions including EOL disposal op- tions. The primary objective of the study was to maximize the dis- counted net profit of the system by gaining periodic revenue from leasing assets. Potter et al. (2011) proposed a set of measures for au- diting purposes to provide a clear picture of CLSC performance by in- vestigating parameters such as, level of product stocks, effect of in- accurate forecasting at organization level and acquisition of high quantities of products before launching from integrated distribution management of mobile phone. The article also identified links between both faulty and non-faulty PRs in design, sourcing, manufacturing and forecasting related to forward supply chain as well as the performance in the integrated condition. Dhib et al. (2016) presented a compro- mising strategy by using entropic analyze, ambiguity notions and co- operative theory in order to evaluate the sustainable performance and decision-making of WEEE management in Tunisia.

Guide and Pentico (2003) presented a closed-loop hierarchical planning model that analyzed financial incentives to control PRs from managerial perspective. The developed model intended to provide decision support in product acquisition, operational planning and control, as well as demand management and product pricing for in a remanufacturing of mobile phone. Wee Kwan Tan et al. (2003) evaluated the performance of a US-based computer manufacturing company which had RL operations in the Asia‐- Pacific region. Authors found that repair, refurbishment, recovery and re- turn management were the major operational RL activities of the company and the critical implementation of IT based system to oversee off-shore PRs was the major performance determinant.

Guide et al. (2008) developed an analytical model for disposition decision driven PR considering time value of returned product, the condition of the product and the impact of congestion at the printer remanufacturing facility. They found that high decay rate coupled with high facility utilization eventually increase the profit of the re- manufacturer. Janse et al. (2010) developed a diagnostic tool that was theoretically and empirically grounded to assess the practice and po- tential improvement of RL activities of a consumer electronic company, from business perspective. The authors identified that strategic part- nerships, performance visibility, top management awareness, strategic focus on PRs, reclaiming value from returns, and prompt supply of re- manufactured products to market were the major areas that a company should concern for performance improvement. Mukhopadhyay and Ma (2009) developed a two stage stochastic programming model to de- termine and evaluate optimal quantity of used products to acquire, and

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

production decisions (i.e. buying new parts from external suppliers) for a remanufacturing firm.

Tan and Kumar (2008) developed a decision making model using LP for manufacturers of computer to access the viability of RL operation under profit maximization condition in Singapore. They found that RL is profitable when return volume of waste computer is high and returns are reused, repaired instead of disposal. Wee Kwan Tan and Kumar (2006) developed a decision making model using system dynamics and simulation for manufacturers of computer to maximize profit that can be achieved from RL operations. The authors found part replacements from hardware suppliers are more profitable than refurbished computer parts. Regardless of return volumes (during processing), viability of the operations can significantly be affected by transportation delay and supplier delay. Ravi et al. (2008) proposed hybrid managerial decision- making activity model using ANP and zero one goal programming (ZOGP) for selecting feasible RL projects for EOL computers according to the available resources of a company.

3.2.3. Product lifecycle perspective Mazhar et al. (2007) presented an integrated approach to estimate

useful remaining life of electrical and electronic components for their reuse using life cycle data analysis, Weibull and artificial neural net- works. With the developed approach, effective life cycle time can be estimated by experimental observation of motor speed, winding tem- perature and power of a washing machine’s electric motor. The authors claimed that the result provided a decision making tool for achieving process and equipment down-time in a CLSC environment focusing on reuse. Chung et al. (2014) analyzed life-cycle costs (LCCs) and life-cycle energy consumption (LCEC) using architecture and supply chain eva- luation method to provide the most beneficial modular structure pro- duct design decisions from lifecycle perspective within CLSC environ- ment.

3.3. Analyzing conceptual framework studies

According to Miles and Huberman (1994), conceptual framework is a visual or written product, one that “explains, either graphically or in narrative form, the main things to be studied—the key factors, con- cepts, or variables—and the presumed relationships among them”. Due to the complexity of EOL product characteristics and the involvement of many different actors in RL/CLSC, new research areas were interlinked by researchers from various disciplines. These studies generally try to construct and suggest a new modeling approach, solution methodology, analyzing approach or evaluation methodology based on a specific problem (Govindan and Soleimani, 2017). For instance, Camgöz-Akdag and Aksoy (2014) proposed a conceptual model for WEEE management considering green-supply-chain management. The findings showed that limited information from the manufacturing firms, finding available data about the outcomes of the system, and the reluctance of firms to share information were found to be major difficulties in implementing a legislation-driven RL system. Some of the studies that used a conceptual framework are described in this section.

3.3.1. RL/CLSC system and/or process focused studies Pimentel et al. (2013) proposed a conceptual model for developing

an RL system in Brazil. Funding, system cost and development re- quirements for the WEEE recycler’s certification were the major com- ponents of the model. From an Asian perspective, Chong et al. (2014) developed a conceptual mathematical model to assess the amount of profit from reselling refurbished computers and components to cover the overall expenses of an EOL computer RL system in Malaysia. Shi et al. (2012) developed a model based on a framework of industrial information integration engineering (IIIE) that focused on application of enterprise systems or e-business systems in the RL process of used batteries, investigating the information flows that can be implemented in designing an RL system. The IIIE was developed back in 2008 as a

new discipline that focuses on application of computing technologies with a wide range of engineering discipline. It represents a set of foundational concepts and techniques that facilitate the industrial in- formation integration process capable of integrating complex informa- tion structure of an engineering system with emerging enabling tech- nology such as Business Process Management (BPM), Service-oriented Architecture (SOA) etc. (Da Xu, 2014).

3.3.2. Remanufacturing-focused El korchi and Millet (2011) introduced a framework that allowed

generation of alternative structures that have less environmental impact and higher economic benefits in RL, with a remanufacturing focus. The authors found that the location of treatment facilities was the key performance indicator of a remanufacturing system when integrated- product forward logistics and reverse-logistics channel-design decisions need to be made. Van Wassenhove and Zikopoulos (2010) developed a conceptual mathematical model to estimate the grading errors occur- ring because of overestimation of the quality of a returned product that affects the optimal procurement decisions of a remanufacturer. Robotis et al. (2005) studied the characteristics of remanufacturing as a tool to develop a secondary market from a reseller’s perspective by developing a conceptual mathematical model. For mobile phones, the authors found that, based on the technology and competition in a market, adding value by remanufacturing and making the used products more attractive to customers can increase resellers’ profits significantly. This way resellers could manage their inventory to serve a secondary market and take important procurement decisions.

3.3.3. Recycling-focused Li et al. (2010) presented a descriptive multi-level management

model to establish an RL coordination mechanism among Chinese re- cycling companies in order to internalize the externalities of recycling, such as air and land pollution, which were often not taken into con- sideration by the policy makers. With the management model, the role and responsibilities of the government departments and manufacturers were highlighted, achieving larger profit from material recovery by WEEE recycling. Walther et al. (2008) developed a conceptual mathe- matical model using LP. Furthermore, the concept of a negotiation approach was implemented into the programming via Lagrangian re- laxation and sub-gradient optimization. In the model, a coordination mechanism was established between one primary recycling company and a group of other recyclers in a recycling network who must meet the obligations of environmental legislation.

3.3.4. Organizational perspective Lei and Qu (2011) analyzed obstacles, necessity, risks and func-

tional modules of an information-sharing platform in a virtual sym- biotic network that allowed WEEE reverse-logistics stakeholders (i.e. member enterprises) to realize effective communication among the members. They found that, if information flow is utilized effectively, an enterprise’s profit, environment benefits and social efficiency could be attained. Atasu and Souza (2013) presented a conceptual deterministic- monopoly demand-model in order to understand the trade-offs in pro- duct recovery that affect a company’s choice (i.e. optimal quality and pricing choices when compared with the benchmark scenario without PR). The authors found that, depending on the form of PR, product quality choice can be better or decline, while product take-back legis- lation can induce an enhancement in quality choice by firms. In addi- tion, it was found that EOL product can be collected either by a retailer collection channel or by the original OEMs. Savaskan and Van Wassenhove (2006) developed a model that focused on the interaction of the manufacturer’s choice of collecting small consumer items such as waste single-use cameras and mobile phones, and strategic product pricing decisions when retailing is competitive.

At present, electronics manufacturers are attempting to create an image of corporate citizenship that reflects their effort to deliver

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

environment-friendly products to customers. Guide and Wassenhove (2001) developed a conceptual analytical framework to analyze the profitability of reuse activities and PR management of the firms that influence the operational requirement of business decisions (i.e. ac- quisition price and the nominal quality of the returned product) in the product-acquisition process. The authors found that product acquisition was the control lever of an EOL PR system, and in reuse activities profitability was a real concern for the firm.

Geyer and Doctori Blass (2010) developed a conceptual and de- scriptive model that summarized the exiting business model of mobile collection, reuse and recycling in the USA. They found that the in- centives given to the manufacturers and refurbishers were not aligned with the environmental-performance-examining reuse case.

3.3.5. Formal and informal sector Ghisolfi et al. (2017) developed a model on social inclusion of in-

formal waste pickers into the environmental policy in the RSC in Brazil. The authors found that, for developing such a system, environmental policy should be restructured according to the country-specific WEEE management agenda, with a high collection rate of used products, ro- bust infrastructure, technology, supply of skilled labor and increase demand for recycled products. Liu et al. (2016) proposed a quality- based price competition model for PR in a dual-channel environment (informal and formal sector recycling). During product acquisition, quality is the single most important factor in determining the acquisi- tion price of returned product for both sectors. In addition, they found that the acquisition price is an important factor in a competitive re- cycling market. When the government subsidy is low, the informal sector is at the forefront in collecting WEEE, while the formal sector has limited penetration in the market for PR. They suggested that the controlling authority should re-adjust the subsidy level for the informal sector and that the sector should only be considered for refurbishing activities.

3.3.6. Product return Srivastava (2008a, 2008b) proposed a model considering the stra-

tegic, operational and customer-service constraints of product returns in the Indian context. Zikopoulos and Tagaras (2007) developed a mathematical model considering RSC that consisted of two collection sites and one refurbishing site that confronted a stochastic demand for refurbished products in a single-period setting. With the model, the authors investigated the impact of uncertainty in an inventory man- agement scenario, when the returned product’s (e.g. computers, prin- ters and mobile phones) quality affected the system profitability. With a conceptual framework based on the Maximum Expected Utility (MEU) principle, Parlikad and McFarlane (2007) showed that the availability of product-specific information has a positive impact on PR. The au- thors also found that Radio-frequency identification (RFID) was an ef- ficient product identification technology that provided efficiency in PR decisions.

3.3.7. Global reverse supply chain and climate change Developing countries have already received an opportunity to get

carbon credit from developed countries under the Clean Development Mechanism (CDM). Research conducted by Caiado et al. (2017) found that WEEE is one of the growing waste streams in developing countries, and with the novel concept of RL carbon credit, developing countries could develop WEEE recycling and disposal infrastructure. Xu et al. (2017) designed a conceptual global reverse-supply-chain (GRSC) model using MILP for WEEE recovery and recycling under various un- certainties (transportation costs and currency exchange rates) and carbon emission constraints, considering transboundary movement of WEEE from Greece to China. Landers et al. (2000) developed a con- ceptual framework of a virtual-warehousing (VW) model for real-time global visibility of logistics assets such as inventory and vehicles. With a case study of a mobile phone company’s effort the authors found that

VW had a significant contribution to repair service when considering transportation, labor costs and service times.

3.4. Analyzing the qualitative studies

Due to a growing environmental concern evolved in customers, industry practitioners and government agencies in product disposal and subsequent operations (i.e. in the RL and CLSC processes), there is a necessity to identify how customers behave to specific actions taken at regional level across the globe. Customers play significant role in RL dispositions (Shumon et al., 2014). The topic regarding the level of awareness towards WEEE and the behavior to dispose of it appro- priately by customers received attention among researchers in shaping RL/CLSC processes. Overall, in this study qualitative refers to the stu- dies conducted via in-depth interviews and surveys, where the re- spondents were customers, companies, and other stakeholders/actors associated in the WEEE RL/CLSC activities. This specific study type can lead to new theory development via practical understanding and knowledge (Govindan et al., 2015).

Jafari et al. (2017) the investigated factors affecting a resident’s behavior in returning WEEE and participating in RL activities in Iran. The authors conducted a questionnaire survey followed by a statistical analysis with logistic regression using Minitab and SPSSS. In the re- search, a consumer’s incentive dependency towards WEEE recycling was characterized, and it was found that household income, household size, education and marital status were important factors in planning formal RL efforts taken by the government. Besides, government’s support in incentives and awareness building programs was found to be crucial for the success of shaping attitudes towards WEEE recycling. Public perception is an important factor in developing an RL model. For example, Cao et al. (2016) estimated the generation of WEEE, as well as public perception and opinion on WEEE management, via material flow analysis (MFA). In regions where WEEE-related data are incomplete, conducting a survey was found to be essential to overcome the limita- tion. The researchers employed a public survey of 1215 respondents to model an RSC for Zhejiang Province in China. They found that in the province people are more inclined to recycle their WEEE items through informal WEEE recyclers.

Recycling was previously analyzed from manufacturers’ and sup- pliers’ point of view, however Gonul Kochan et al. (2016) reported that the customer perspective in recycling was analyzed for the first time in their research that implies a holistic approach to develop a RL model. To assess recycling behavior in line with the Theory of Reasoned Action (TRA), the authors surveyed 327 university students. Structural-equa- tion modeling was utilized for analysis and they found that attitudes and moral norms act as driving forces in WEEE recycling. Perceived convenience was also considered as an important factor that creates more involvement in the process.

Dixit and Badgaiyan (2016) found that perceived behavioral con- trol, subjective norms, moral norms and willingness to sacrifice unused items act as antecedents to the return behavior of customers returning their waste mobile phones. For analysis, the authors constructed a structural-equation model where the Theory of Planned Behavior (TPB) was implemented. The authors urged that government and non-gov- ernment organizations (NGOs) have a great impact in changing the social views and attitudes of customers, which may have a positive impact on WEEE RL processes. Disposal behavior can positively influ- ence the increased level of return, which can be capitalized on by RL managers in acquiring more WEEE from customers.

Demajorovic et al. (2016) conducted an exploratory qualitative re- search to identify major challenges and barriers to implementing an RL model for computers and mobile phones in Brazil. The technological gap in recycling industries, continental dimensions (as a developing country), taxation challenges and conflicts between waste picker or- ganizations and the industry were found as the major challenges in developing a sustainable RL system. Dixit and Vaish (2013) examined

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

the impact of demographic variables, namely age, gender, income and place of residence, on post-consumption disposal choices of urban In- dian consumers for their mobile wastes in identifying the antecedents of consumer behavior that act to develop an effective RL system.

Hanafi et al. (2013) identified three performance indicators of a waste mobile-phone collection pilot project in Indonesia, namely par- ticipation rate, return rate, and cost. The authors found that, even though a formal recycling channel was created in the city of Jakarta, customers still felt reluctant to participate in the program because of the high presence of informal-sector WEEE recycling. The performance of the WEEE project in the developing country’s context can be boosted through increasing publicity and building partnerships among elec- tronic retailer, government and telecommunication companies. Agarwal et al. (2012) studied the customer return behavior of WEEE items at different financial incentive levels and attempted to in- corporate the latest practices into their research. Initial data collection was done by a sample survey. By developing an optimization model using particle-swarm optimization (PSO) algorithms and the simulation package ARENA, they identified that product and component reliability were critical in developing a customer incentives policy.

Based on survey questionnaires for Taiwan's electronics industry Chiou et al. (2012), identified factors of RL implementation - environ- mental regulations and directives, consumer's environmental aware- ness, competition among stakeholders. The factors were ranked using the FAHP method with a focus on environmental management. Kissling et al. (2012) illustrated a definition of a typological operating model of reuse focusing on two WEEE items, namely Information and Commu- nication Technologies (ICT) equipment and whitegoods, considering four dimensions of reuse structure: supply chain, offer, customers and finance. The authors developed the model to understand the complex structure and dynamics of the reuse sector in Latin America, Africa, North America and Europe, thus providing a concise description of reuse activities and outcomes in the continents.

Lee et al. (2007) investigated the perception gap of RL service quality for the mobile-phone industry in Taiwan using a PZB model, which generally identifies the gaps between the service-quality ex- pectation of customers and an organization’s performance on service quality. Accurate pricing, motivation towards high recycling, free-of- charge product upgrading within warranty period, convenient location for product return and exchange, free repairing, and finally post-repair notice were found to be crucial for a mobile-phone RL service model. Hung Lau and Wang (2009) investigated whether the Chinese electro- nics industry is performing RL activities according to the current RL theories and models, mainly with the focus of promotion of corporate image, fulfillment of obligation for environment protection, and im- provement of customer service. The authors found that low public awareness on environmental protection, underdevelopment of re- cycling technologies and lack of enforceable regulations were the cri- tical barriers for RL implementation.

Queiruga et al. (2008) evaluated the appropriateness of the WEEE recycling sites in Spain using a discrete MCDM method- PROMETHEE (Preference Ranking Organization METHod for Enrichment Evalua- tions). The factors that were considered for selecting plant site locations were economic objectives (e.g. land cost, personnel costs, energy price), Infrastructural objectives (e.g. facility access, agglomeration effects, proximity to inhabited areas, absence of other WEEE recycling plants and availability of labor) and legal objectives (e.g. availability of a local waste-processing program and environmental grant). Autry et al. (2001) investigated RL performance and satisfaction from a catalog retailing perspective which were influenced by sales volume, firm size, customers’ satisfaction and disposition. The performance had an impact on the sales volume, while industry effects (e.g. market structure) sig- nificantly impacted satisfaction. On the other hand, the location of the responsibility for disposition had no significant impact on performance and satisfaction.

4. Analysis of research gap and future research directions

Several issues were identified as potential research avenues for the future. The above description and detailed analysis of the articles cre- ated a comprehensive knowledge base on the overall RL/CLSC of WEEE sector. After careful consideration, research gaps were identified and future research directions are given as below:

• Even though both RL and CLSC research focusing on WEEE is in- creasing over the years, there is a lack in progress of the CLSC network design. A more integrated approach, considering both the FL and RL of WEEE is required. Although a few studies were con- ducted in the CLSC area, most of them were based on a generic framework, and often the authors of the articles urged for more empirical research based on real-world scenarios.

• Among the main research fields in the studies, the designing and planning of reverse distribution is the most researched topic, as it contains some of the critical topics such as open-loop and closed- loop network design. Future researchers should consider conducting qualitative research in the field. Qualitative, especially survey- based, research provides an in-depth understanding of the practical problems that lead to theory development (Govindan et al., 2015). In addition, there is a serious lack of specifying the source of WEEE generation, which should be included in designing the RL network. Generally, WEEE generation is characterized by three types of sources – households, government organizations and institutions, and the business sector. Source specific WEEE RL network design could provide valuable policy implications for responsible autho- rities managing their RL network with better economic and en- vironmental performance. This was also evident from Fig. 11 when considering source as one of the uncertainties in network designing.

• In the OLND, recycling is the most important disposition considered by the articles, however, there is a scope for future researchers to consider recycling in CLSC networks (using coordinated approach with other firms using secondary raw materials), where economic efficiency, environmental cost and environmental impacts need to be included in the objective functions of RL modeling. Furthermore, there is a need for investigation of other alternatives – reuse and repair in the network. No single research was found that considered recycling, remanufacturing, reuse and repair in an integrated manner. On the other hand, MILP was the most utilized modeling approach, with alternatively stochastic and fuzzy programming approaches. However, in future, when MILP/MIP is being utilized, strategic management, environment legislation, customer service, and asset management can be included as modeling objectives for RL network design. In real-world scenarios, a number of complex and uncertain variables may arise in computation. When the number of variables and constraints increases in modeling, meta- heuristic algorithms like GA or heuristic integer programming, for instance a scatter search, can be implemented (Amin and Zhang, 2012). In collaborative planning, application of GP in CLSC network modeling could be an interesting research (Gupta and Evans, 2009). In addition, for better computational performance in algorithm- based research, heuristic, meta-heuristic, approximations and a sampling-based solution approach can be employed for a large number of scenario-based problems. EOL product management from the RL/CLSC perspective is scarce in the literature. In developing models, decomposition and heuristic approaches can be im- plemented for this particular field. To improve the reusability and recyclability of WEEE, the eco-design concept has the potential to integrate into RL/CLSC network design. Additionally, simulation- based collection processes in an RL network should be considered for research in the area of DPRD studies.

• There is a clear deficit of implementing a multi-level and/or multi- objective and/or multi-period modeling approach in RL networks. Tactical objectives such as return forecasting, product return

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

handling and aggregate production planning; and operational level objectives, for instance vehicle planning and scheduling, optimal disassembly sequences of remanufacturing processes, should be in- cluded more in modeling open-loop RL networks. Multi-period nondeterministic modeling in WEEE product recovery networks needs further investigation. Likewise, inventory management of CLSC networks along with strategic safety stocks of RL considering remanufacturing in particular, is another research direction. Multi- objective programming considering risks and resource savings should be included in RL network modeling.

• In terms of uncertainty, the cost of remanufacturing/recycling, the price of remanufactured product, revenue, volume of return (quantity), time of returns, quality, capacity of facilities (e.g. treat- ment, recycling, remanufacturing), WEEE generation rate (location specific), the market need/demand for recycled products should be introduced in RL network models. In particular, the product return rate in multi-period CLSC networks with an interaction of demand could be an interesting topic of research in future. In addition, the price of remanufactured product based on market demand is an- other research area in WEEE CLSC network design. Considering demand as probabilistic function can be included in modeling. Sensitivity analysis can be included in studies that deal with rela- tively low-volume products in terms of return (e.g. products with longer lifecycle). For products with a shorter lifecycle, fuzzy-set theories can be implemented. Uncertainty in the WEEE recycling network such as quantity in conjunction with transportation cost is a potential area of research. In addition, during development of RL network infrastructure, strategic planning tools, such as balanced scorecard, and simulation tools can be implemented when such uncertainties arise. Environmental influence and supplier selection are two less considered uncertainty parameters (shown in Fig. 11) in network modeling that could be an interesting topic for future re- search.

• From Fig. 10, it is clear that sustainability dimensions – social, economic and environmental - were considered by very few articles (only 3 papers), whereas economic issues (e.g. cost, price, revenue etc.) were the most prominent dimensions (considered by 75% of the papers). In further research, WEEE RL/CLSC network design should introduce all the three dimensions, in particular the social impact to understand the overall intrinsic benefits of the network in a particular region and/or country.

• It will be interesting if another new dimension – technology under the sustainability context - is considered. This could provide a more holistic insight of the RL/CLSC system itself as well as achieving an overall goal of sustainable development. From technical standpoint, specifically, the impact of RFID and ICT-based network support systems implementing the concept of internet of things (IoT), for inventory management and product-recovery information manage- ment system development, could be a new area of research in this context. This might provide better information flow among all ac- tors. In all supply chain network of E-waste management, IoT has a crucial role in resource savings at low cost (Nobre and Tavares, 2017). As social sustainability saw less research, new parameters under this criteria, such as public health and safety, can be included in developing the RL network model using a game-theory method where the preferences and participation of customers and govern- ment as actors can be included in models. Another important per- spective that needs further research is customer participation in determining recycling fees and quantity generation in an RL system, creating a competitive EEE market.

• Further research should be carried out in the area of 3PRLP selection and VRP. In the first area, large-scale empirical studies with multi- WEEE product scenarios should be initiated. On the other hand, in 3PRLP studies, reverse channel choice by small and large companies according to profits and cost were the highest priority in the past. However, there is a lack of study in developing a comprehensive

framework under which several RL processes such as product ac- quisition, repair, reuse and remanufacturing need to be performed by 3PRLP. Furthermore, the impact of legislative initiatives on the performance of 3PRLP considering all sustainability dimensions needs further investigation. In addition, the negative impact of 3PRLP inclusion by OEMs and the interaction of small companies in a sustainable CLSC system should be investigated, rather than only RL operation. The collaboration between small and large companies in RL management, in other words outsourcing, should be a future research topic. As limited research was conducted in vehicle routing, one of the research directions could be to observe the im- pact of disassembly systems in vehicle routing. The environmental performance of vehicle routing, for instance reduction of CO2 emission with distances during transportation and collection, was a less-researched topic. Classical vehicle routing problems can use Tabu search and scatter search with sensitivity analysis for holistic analysis of a specific problem. Routing design is often concerned with the length and number of tours, and can be solved by im- plementing GRASP and MIP or even a global information system (GIS) system.

• In the category of decision making and performance evaluation, the product lifecycle perspective received less attention among the re- searchers. RL processes such as disassembly and inspection demand environmentally and economically optimum product design, by which both time and cost in the overall RL system could be saved and/or minimized. As seen earlier, most of the articles were con- cerned with the economic aspects of the RL and CLSC of WEEE. However, when considering environmental aspects, there is a need to consider the use of two specific modeling techniques: LCA and MFA. A limited number of papers considered these approaches, and future researchers should consider them. From the circular-economy and efficient-resource-utilization perspectives, which top manage- ment of recycling and remanufacturing firms struggle to consider, using these tools (LCA and MFA) could tremendously assist in minimizing the total cost and maximizing the environmental per- formance of the RL and CLSC process. These tools are also able to provide valuable information on the available critical raw materials that can be recovered, and potential mitigation of greenhouse gas/ CO2 emission (as a measure of environmental performance) for ef- ficient and effective RL operations. Moreover, the impact of un- certainty parameters, such as the capacity level in facilities, cost and collection rate, on the lifecycle performance in a CLSC environment could be the most promising research direction for the future. Another interesting future research topic could be implementing game theory to investigate interactions in decision making among different players within the RL or CLSC for WEEE.

• In the conceptual-framework based studies, relatively less attention was given to RL processes - reuse and repair. The impact of these two alternatives on overall RL management organized by manu- facturers could be interesting future research direction. For the case of recycling, there is a need for open-sourced online-based market information system that can determine the recycling fees of a pro- duct in an RL system where WEEE would be collected by OEMs or by recycling firms. In addition, research could be considering the im- pact of regulatory instruments such as EPR, with the interaction of the formal and informal sectors on WEEE collection and recycling.

• Disposal rate (frequency), type of WEEE items disposed, average lifetime of disposed product, storage time, customer awareness, willingness to pay (WTP), top-management attitude (from com- pany’s perspective) are some of the critical issues that need to be addressed at a regional level to develop sustainable RL/CLSC sys- tems. In such a context, qualitative studies could be a successful research methodology which needs further implementation.

• There is a lack of product/case-specific WEEE RL network modeling initiatives among the studies. Future research should consider more product-oriented studies such as for waste batteries, IT equipment

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

(e.g. laptop computers, printers, cell phones, telephones, personal digital assistant products (PDAs), ipads, and tablets), small con- sumer electronics (e.g. portable music-players, toys) and white- goods. Computer waste recycling, reuse and remanufacturing (in an integrated manner) based RL/CLSC networks should have particular attention.

• Future researchers should envision utilizing the concept of circular economy in developing infrastructure and formulating sustainable RL/CLSC activities at national level. Current body of literature often fails to collaborate these issues. As the WEEE generation is growing exponentially to almost every country in the world, the integrated understanding of sustainability, circular economy and CLSC from supply-chain management perspective is an important research avenue to explore.

• There are also potential ways of improving this review article itself. Taking a higher number of research articles from grey sources, such as company reports, annual reports, white papers and online sources could enrich the content. Another improvement could be categor- ization of contents by geographical locations, qualitative vs. quan- titative approaches and different modeling approaches and solution methodologies.

5. Conclusion

This paper presents a comprehensive literature review of recent papers published at different scientific journals in RL/CLSC issues that considered WEEE or E-waste as an EOL product. A total of 157 papers published in the international peer-reviewed journals, conference pro- ceedings, and book chapters during the period 1999-2017 are selected, categorized, analyzed and reviewed. After reviewing, several research gaps were identified with important implications for future research. The authors think that this review provides a holistic overview of the whole system perspective on the research field, and identifying key future research directions would be useful for researchers. The cate- gorizations and citied references may be utilized as a broad frame of references to advance concepts and models for the future research.

Empirical research focusing on CLSC network design considering real-world scenarios is a suggested future research opportunity. To understand the dynamics of source specific (i.e. households, organiza- tions and businesses) WEEE generation and its management, qualita- tive, especially survey-based research is recommended. All the dis- position alternatives (i.e. recycling, remanufacturing, reuse and repair) should be considered in an integrated manner in designing CLSC net- work for future research. In RL and CLSC network designing, lack of studies considering different modeling objectives, problem formulation and solution approaches are discussed and future opportunities are advised. Scope of utilizing multi-objective programming considering different uncertainty parameters is highlighted and prescribed for fur- ther research. In future RL and CLSC network modeling, attentions should be given to specific WEEE items (IT equipment, small consumer electronics etc.), environmentally friendly-3PRLP selection, technology integrated network support system (by extensive implementation of IoT) and application of LCA and MFA tools for attaining highest eco- nomic and environmental performance. In holistic E-waste RL and CLSC system development, integration of sustainability and circular economy concepts is the broad future research area that will ensure sustainable waste management, resource conservation, material recovery and mi- tigation of environmental impact.

Acknowledgement

The authors would like to thank Dr Keith Imrie for proof-reading this paper. The authors like to thank anonymous reviewers for their constructive and valuable comments for improving the manuscript. The first author acknowledges the financial support from Macquarie University under the scholarship scheme “International Macquarie

University Research Training Program (iMQRTP)” for conducting this research.

Appendix A

List of AbbreviationsANPAnalytic network processBSCBalanced ScorecardCLSCClosed-loop supply chainCRMCritical raw materials- CLNDClosed-loop network designCCsCollection centersCLDSCClosed- loop distribution supply chainCDMClean Development Mechanism- DPRLDesigning and planning of reverse distributionDEAData Envelopment AnalysisDfEDesign-for-the-environmentDSSDecision sup- port systemEOLEnd-of-lifeEPRExtended producer responsibilityE- wasteElectronic wasteECAElection campaign algorithmEPCElectronic Product CodeFLForward logisticsFSCForward supply chainFAHPFuzzy analytic hierarchy processGAGenetic algorithmGAMSGeneral Algebraic Modeling SystemGRASPGreedy Randomized Adaptive Searching ProcedureGPGoal-programmingGRSCGlobal reverse supply chainILPInteger linear programmingIPInteger programming- ITInformation technologyICTInformation and Communication TechnologiesLPLinear programmingLINMAPLinear programming tech- nique for multi-dimensional analysis of preferenceLCALifecycle assessmentLCCsLifecycle costsLCECLifecycle energy consumption- MILPMixed-integer linear programmingMCDMMulti-Criteria Decision MakingMIPMixed integer programmingMINLPMixed integer non- linear programmingMEUMaximum expected utilityprogramming- MICMACMatriced’ Impacts Croise's Multiplication Appliquée a UN ClassementNSGANon-dominated Sorting Genetic AlgorithmNL PANon-linear programming algorithmOECDOrganisation for Economic Co-operation and DevelopmentOLNDOpen-loop network designOEMs- Original equipment manufacturersPLCProduct lifecyclePSO Particle swarm optimizationRLReverse logisticsRSCReverse supply chainRPRecycling plantRCsRecycling centersRNRecovery networkRLNDReverse logistics network designRNARecovery network arrangementRCPSPResource constrained project scheduling problemRFIDRadio-frequency identificationSCsSorting centersSAA- Sample average approximationSDPStochastic dynamic programm ing3PRLPThird-party reverse logistics providerTRATheory of Reasoned ActionTSsTransfer stationsTFsTreatment facilitiesTOPSISThe Technique for Order of Preference by Similarity to Ideal SolutionTFT- LCDThin-film-transistor liquid- crystal displayVRVehicle routing- VRPVehicle routing problemVWVirtual warehousingWEEEWaste elec- trical and electronic equipmentZOGPZero-one goal programming

References

Achillas, C., Aidonis, D., Vlachokostas, C., Moussiopoulos, N., Banias, G., Triantafillou, D., 2012. A multi-objective decision-making model to select waste electrical and elec- tronic equipment transportation media. Resources, Conservation and Recycling 66, 76–84.

Achillas, C., Vlachokostas, C., Moussiopoulos, N., Banias, G., 2010. Decision support system for the optimal location of electrical and electronic waste treatment plants: A case study in Greece. Waste Management 30 (5), 870–879.

Agarwal, G., Barari, S., Tiwari, M.K., 2012. A PSO-based optimum consumer incentive policy for WEEE incorporating reliability of components. International Journal of Production Research 50 (16), 4372–4380.

Agrawal, S., Singh, R.K., Murtaza, Q., 2015. A literature review and perspectives in re- verse logistics. Resources, Conservation and Recycling 97, 76–92.

Ahluwalia, P.K., Nema, A.K., 2006. Multi-objective reverse logistics model for integrated computer waste management. Waste management & research 24 (6), 514–527.

Akçalı, E., Çetinkaya, S., Üster, H., 2009. Network design for reverse and closed‐loop supply chains: An annotated bibliography of models and solution approaches. Networks 53 (3), 231–248.

Alumur, S.A., Nickel, S., Saldanha-da-Gama, F., Verter, V., 2012. Multi-period reverse logistics network design. European Journal of Operational Research 220 (1), 67–78.

Amin, S.H., Zhang, G., 2012. A proposed mathematical model for closed-loop network configuration based on product life cycle. The International Journal of Advanced Manufacturing Technology 58 (5), 791–801.

Amin, S.H., Zhang, G., 2013. A three-stage model for closed-loop supply chain config- uration under uncertainty. International Journal of Production Research 51 (5), 1405–1425.

Arabi, M., Mansour, S., Shokouhyar, S., 2017. Optimizing a warranty–based sustainable product service system using game theory. International Journal of Sustainable

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0005

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0010

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0015

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0020

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0025

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0030

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0035

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0040

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0045

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0050

Engineering 1–12. Assavapokee, T., Wongthatsanekorn, W., 2012. Reverse production system infrastructure

design for electronic products in the state of Texas. Computers & Industrial Engineering 62 (1), 129–140.

Atasu, A., Souza, G.C., 2013. How does product recovery affect quality choice? Production and Operations Management 22 (4), 991–1010.

Atasu, A., Toktay, L.B., Van Wassenhove, L.N., 2013. How collection cost structure drives a manufacturer’s reverse channel choice. Production and Operations Management 22 (5), 1089–1102.

Autry, C.W., Daugherty, P.J., Glenn Richey, R., 2001. The challenge of reverse logistics in catalog retailing. International Journal of Physical Distribution & Logistics Management 31 (1), 26–37.

Ayvaz, B., Bolat, B., 2014. Proposal of a Stochastic Programming Model for Reverse Logistics Network Design under Uncertainties. International Journal of Supply Chain Management 3 (3), 33–42.

Ayvaz, B., Bolat, B., Aydın, N., 2015. Stochastic reverse logistics network design for waste of electrical and electronic equipment. Resources, Conservation and Recycling 104 (Part B), 391–404.

Baldé, C.P., Forti, V., Gray, V., Kuehr, R., Stegmann, P., 2017. The Global E-waste Monitor – 2017. United Nations University (UNU). International Telecommunication Union (ITU) & International Solid Waste Association (ISWA), Bonn/Geneva/Vienna.

Bereketli, I., Erol Genevois, M., Esra Albayrak, Y., Ozyol, M., 2011. WEEE treatment strategies’ evaluation using fuzzy LINMAP method. Expert Systems with Applications 38 (1), 71–79.

Cagno, E., Magalini, F., Trucco, P., 2008. Modelling and planning of Product Recovery Network: the case study of end-of-life refrigerators in Italy. International Journal of Environmental Technology and Management 8 (4), 385–404.

Caiado, N., Guarnieri, P., Xavier, L.H., de Lorena Diniz Chaves, G., 2017. A character- ization of the Brazilian market of reverse logistic credits (RLC) and an analogy with the existing carbon credit market. Resources, Conservation and Recycling 118, 47–59.

Camgöz-Akdag, H., Aksoy, H.M., 2014. Green supply chain management for electric and electronic equipment: case study for Turkey. In: Huntsville. Paper presented at the Proceedings of the International Annual Conference of the American Society for Engineering Management.

Cao, J., Chen, Y., Shi, B., Lu, B., Zhang, X., Ye, X., Zhai, G., Zhu, C., Zhou, G., 2016. WEEE recycling in Zhejiang Province, China: generation, treatment, and public awareness. Journal of Cleaner Production 127, 311–324.

Cao, S., Zhang, K., 2011. Optimization of the flow distribution of e-waste reverse logistics network based on NSGA II and TOPSIS. 6–8 May 2011. Paper presented at the 2011 International Conference on E-Business and E-Government (ICEE).

Chandiran, P., Surya Prakasa Rao, K., 2008. Design of reverse and forward supply chain network: a case study. International Journal of Logistics Systems and Management 4 (5), 574–595.

Chang, X., Huo, J., Chen, S., 2006. Study on integrated logistics network model and network design for waste electrical and electronic equipment. Paper presented at the Service Operations and Logistics, and Informatics, 2006. SOLI'06. IEEE International Conference on.

Chen, H., He, H., 2010. Reverse logistics demand forecasting under demand uncertainty, ICLEM 2010: Logistics For Sustained Economic Development: Infrastructure, Information, Integration. 343–348.

Chen, Y., Chan, F., Chung, S., 2015. An integrated closed-loop supply chain model with location allocation problem and product recycling decisions. International Journal of Production Research 53 (10), 3120–3140.

Cheng, Y.-H., Lee, F., 2010. Outsourcing reverse logistics of high-tech manufacturing firms by using a systematic decision-making approach: TFT-LCD sector in Taiwan. Industrial Marketing Management 39 (7), 1111–1119.

Chiou, C.Y., Chen, H.C., Yu, C.T., Yeh, C.Y., 2012. Consideration Factors of Reverse Logistics Implementation -A Case Study of Taiwan’s Electronics Industry. Procedia - Social and Behavioral Sciences 40, 375–381.

Choi, J.K., Fthenakis, V., 2010. Economic feasibility of recycling photovoltaic modules. Journal of Industrial Ecology 14 (6), 947–964.

Chong, S.H., Pauline, O., Sulaiman, H., 2014. Reverse Logistics Network Design for End of Life Computer in Malaysia. Paper presented at the Advanced Materials Research.

Chopra, S., Meindl, P., 2007. Supply chain management. Strategy, planning & operation. Das summa summarum des management 265–275.

Chouinard, M., D’Amours, S., Aït-Kadi, D., 2008. A stochastic programming approach for designing supply loops. International Journal of Production Economics 113 (2), 657–677.

Chung, W.-H., Kremer, G.E.O., Wysk, R.A., 2014. A modular design approach to improve product life cycle performance based on the optimization of a closed-loop supply chain. Journal of Mechanical Design 136 (2), 021001–021020.

Da Xu, L., 2014. Engineering Informatics and Industrial Information Integration Engineering. Paper presented at the Enterprise Systems Conference (ES), 2014.

Dantzig, G.B., Ramser, J.H., 1959. The truck dispatching problem. Management science 6 (1), 80–91.

Dat, L.Q., Truc Linh, D.T., Chou, S.-Y., Yu, V.F., 2012. Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products. Expert Systems with Applications 39 (7), 6380–6387.

Demajorovic, J., Augusto, E., Souza, M., 2016. Reverse logistics of e-waste in developing countries: challenges and prospects for the Brazilian model. Ambiente & Sociedade 19 (2), 117–136.

Deng, C.-L., Shao, C.-M., 2009. Multi-Product Min-Cost Recycling Network Flow Problem,

in: Chou, S.-Y., Trappey, A., Pokojski, J.,Smith, S. (Eds.), Global Perspective for Competitive Enterprise, Economy and Ecology: Proceedings of the 16th ISPE International Conference on Concurrent Engineering. Springer London, London, pp. 653-661.

Dhib, S., Addouche, S.-A., El Mhamdi, A., Loukil, T., 2016. Performance Study for a Sustainable Strategy: Case of Electrical and Electronic Equipments Waste, in: Bouras, A., Eynard, B., Foufou, S.,Thoben, K.-D. (Eds.), Product Lifecycle Management in the Era of Internet of Things: 12th IFIP WG 5.1 International Conference, PLM 2015, Doha, Qatar, October19-21, 2015, Revised Selected Papers. Springer International Publishing, Cham, pp. 572-587.

Directive, E., 2012. Directive 2012/19/EU of the European Parliament and of the Council of 4 July 2012 on waste electrical and electronic equipment. WEEE. Official Journal of the European Union L 197, 38–71.

Dixit, S., Badgaiyan, A.J., 2016. Towards improved understanding of reverse logistics – Examining mediating role of return intention. Resources, Conservation and Recycling 107, 115–128.

Dixit, S., Vaish, A., 2013. Sustaining environment and organisation through e-waste management: a study of post consumption behaviour for mobile industry in India. International Journal of Logistics Systems and Management 16 (1), 1–15.

Easterby-Smith, M., Thorpe, R., Jackson, P.R., 2012. Management research. Sage. Easwaran, G., Üster, H., 2010. A closed-loop supply chain network design problem with

integrated forward and reverse channel decisions. IIE Transactions 42 (11), 779–792. El korchi, A., Millet, D., 2011. Designing a sustainable reverse logistics channel: the 18

generic structures framework. Journal of Cleaner Production 19 (6–7), 588–597. Elbadrawy, R., Moneim, A.F.A., Fors, M.N., 2015. E-waste reverse logistic optimization in

Egypt. 3-5 March 2015. Paper presented at the 2015 International Conference on Industrial Engineering and Operations Management (IEOM).

Eurostat, 2018. END OF LIFE VEHICLES (ELVS). (Accessed (March 9 2018). http://ec. europa.eu/environment/waste/elv/index.htm.

Fernandes, A.S., Gomes-Salema, M.I., Barbosa-Povoa, A.P., 2010. The retrofit of a closed- loop distribution network: the case of lead batteries. Computer Aided Chemical Engineering 28, 1213–1218.

Fernández, I., de la Fuente, D., Mosterín, J.L., Suarez, M., 2006. Optimization of a Reverse Logistics Network within the Ambit of Mobile Phones. Paper presented at the IC-AI.

Ferrer, G., Ketzenberg, M.E., 2004. Value of information in remanufacturing complex products. IIE transactions 36 (3), 265–277.

Fleischmann, M., Beullens, P., Bloemhof‐Ruwaard, J.M., Wassenhove, L.N., 2001. The impact of product recovery on logistics network design. Production and Operations Management 10 (2), 156–173.

Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., Van der Laan, E., Van Nunen, J.A., Van Wassenhove, L.N., 1997. Quantitative models for reverse logistics: A review. European journal of operational research 103 (1), 1–17.

Franke, C., Basdere, B., Ciupek, M., Seliger, S., 2006. Remanufacturing of mobile pho- nes—capacity, program and facility adaptation planning. Omega 34 (6), 562–570.

Galbreth, M.R., Blackburn, J.D., 2010. Offshore remanufacturing with variable used product condition. Decision Sciences 41 (1), 5–20.

Gallo, M., Murino, T., Romano, E., 2010. The Simulation of Hybrid Logic in Reverse Logistics Network. Selected Topics in System Science and Simulation Engineering 378–384.

Gamberini, R., Gebennini, E., Manzini, R., Ziveri, A., 2010. On the integration of planning and environmental impact assessment for a WEEE transportation network—A case study. Resources, Conservation and Recycling 54 (11), 937–951.

Geissdoerfer, M., Savaget, P., Bocken, N.M., Hultink, E.J., 2017. The Circular Economy–A new sustainability paradigm? Journal of Cleaner Production 143, 757–768.

Geyer, R., Doctori Blass, V., 2010. The economics of cell phone reuse and recycling. The International Journal of Advanced Manufacturing Technology 47 (5), 515–525.

Ghisolfi, V., Diniz Chaves, G.L., Ribeiro Siman, R., Xavier, L.H., 2017. System dynamics applied to closed loop supply chains of desktops and laptops in Brazil: A perspective for social inclusion of waste pickers. Waste Management 60, 14–31.

Gold, S., Seuring, S., Beske, P., 2010. Sustainable supply chain management and inter‐- organizational resources: a literature review. Corporate social responsibility and environmental management 17 (4), 230–245.

Golinska, P., Kawa, A., 2011. Recovery Network Arrangements: The WEEE Case. Information Technologies in Environmental Engineering 579–591.

Gomes, M.I., Barbosa-Povoa, A.P., Novais, A.Q., 2011. Modelling a recovery network for WEEE: A case study in Portugal. Waste Management 31 (7), 1645–1660.

Gonul Kochan, C., Pourreza, S., Tran, H., Prybutok, V.R., 2016. Determinants and logistics of e-waste recycling. The International Journal of Logistics Management 27 (1), 52–70.

Govindan, K., Soleimani, H., 2017. A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of Cleaner Production 142, 371–384.

Govindan, K., Soleimani, H., Kannan, D., 2015. Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research 240 (3), 603–626.

Grant, D.B., Banomyong, R., 2010. Design of closed-loop supply chain and product re- covery management for fast-moving consumer goods: The case of a single-use camera. Asia Pacific Journal of Marketing and Logistics 22 (2), 232–246.

Grunow, M., Gobbi, C., 2009. Designing the reverse network for WEEE in Denmark. CIRP Annals - Manufacturing Technology 58 (1), 391–394.

Guerra, L., Murino, T., Romano, E., 2009. Reverse logistics for electrical and electronic equipment: a modular simulation model. Proceedings of the 8th Recent Advances in System Science and Simulation in Engineering ICOSSSE 2009. pp. 307–312.

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0050

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0055

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0060

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0065

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0070

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0075

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0080

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0085

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0090

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0095

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0100

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0105

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0110

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0115

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0120

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0125

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0130

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0135

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0140

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0145

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0150

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0155

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0160

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0165

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0170

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0175

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0180

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0185

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0190

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0205

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0210

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0215

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0220

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0225

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0230

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0235

http://ec.europa.eu/environment/waste/elv/index.htm

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0245

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0250

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0255

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0260

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0265

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0270

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0275

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0280

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0285

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0290

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0295

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0300

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0305

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0310

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0315

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0320

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0325

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0330

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0335

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0340

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0345

Guide Jr, V.D., Pentico, D., 2003. A hierarchical decision model for re-manufacturing and re-use. International Journal of Logistics 6 (1-2), 29–35.

Guide Jr, V.D.R., Van Wassenhove, L.N., 2009. OR FORUM—The evolution of closed-loop supply chain research. Operations Research 57 (1), 10–18.

Guide, V.D.R., Gunes, E.D., Souza, G.C., Van Wassenhove, L.N., 2008. The optimal dis- position decision for product returns. Operations Management Research 1 (1), 6–14.

Guide, V.D.R., Wassenhove, L.N., 2001. Managing product returns for remanufacturing. Production and Operations Management 10 (2), 142–155.

Zhi, Guo-jian, Dong, X.-b., Zhang, R.-x., 2010. Application of genetic algorithms for the design of WEEE logistics network model. 22–24 Oct. 2010. Paper presented at the 2010 International Conference on Intelligent Computing and Integrated Systems.

Gupta, A., Evans, G.W., 2009. A goal programming model for the operation of closed-loop supply chains. Engineering Optimization 41 (8), 713–735.

Hammond, D., Beullens, P., 2007. Closed-loop supply chain network equilibrium under legislation. European Journal of Operational Research 183 (2), 895–908.

Hanafi, J., Christiani, A., Kristina, H.J., Utama, K.P., 2013. Collecting End-of-Life Mobile Phones in Jakarta: A Pilot, in: Nee, A.Y.C., Song, B.,Ong, S.-K. (Eds.), Re-engineering Manufacturing for Sustainability: Proceedings of the 20th CIRP International Conference on Life Cycle Engineering, Singapore 17-19 April, 2013. Springer Singapore, Singapore, pp. 365-370.

Hanqing, L., Ru, Y., 2009. The models of the reverse 3PL in the treatment of the waste electrical and electronic equipment. Paper presented at the Proceedings of the 2nd International Conference on Modelling and Simulation, ICMS2009.

Hong, I.H., Yeh, J.-S., 2012. Modeling closed-loop supply chains in the electronics in- dustry: A retailer collection application. Transportation Research Part E: Logistics and Transportation Review 48 (4), 817–829.

Hung Lau, K., Wang, Y., 2009. Reverse logistics in the electronic industry of China: a case study. Supply Chain Management: An International Journal 14 (6), 447–465.

Iacovidou, E., Millward-Hopkins, J., Busch, J., Purnell, P., Velis, C.A., Hahladakis, J.N., Zwirner, O., Brown, A., 2017. A pathway to circular economy: Developing a con- ceptual framework for complex value assessment of resources recovered from waste. Journal of Cleaner Production 168, 1279–1288.

Işıldar, A., Rene, E.R., van Hullebusch, E.D., Lens, P.N.L., 2017. Electronic waste as a secondary source of critical metals: Management and recovery technologies. Resources, Conservation and Recycling.

Islam, M.T., Abdullah, A., Shahir, S., Kalam, M., Masjuki, H., Shumon, R., Rashid, M.H., 2016. A public survey on knowledge, awareness, attitude and willingness to pay for WEEE management: Case study in Bangladesh. Journal of Cleaner Production 137, 728–740.

Jafari, A., Heydari, J., Keramati, A., 2017. Factors affecting incentive dependency of residents to participate in e-waste recycling: a case study on adoption of e-waste reverse supply chain in Iran. Environment, Development and Sustainability 19 (1), 325–338.

Janse, B., Schuur, P., de Brito, M.P., 2010. A reverse logistics diagnostic tool: the case of the consumer electronics industry. The International Journal of Advanced Manufacturing Technology 47 (5), 495–513.

Jayaraman, V., 2006. Production planning for closed-loop supply chains with product recovery and reuse: an analytical approach. International Journal of Production Research 44 (5), 981–998.

Jayaraman, V., Guide Jr, V., Srivastava, R., 1999. A closed-loop logistics model for re- manufacturing. Journal of the Operational Research Society 50 (5), 497–508.

Kannan, G., 2009. Fuzzy approach for the selection of third party reverse logistics pro- vider. Asia Pacific Journal of Marketing and Logistics 21 (3), 397–416.

Kannan, G., Sasikumar, P., Devika, K., 2010. A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied Mathematical Modelling 34 (3), 655–670.

Kara, S., Rugrungruang, F., Kaebernick, H., 2007. Simulation modelling of reverse lo- gistics networks. International Journal of Production Economics 106 (1), 61–69.

Kawa, A., Golinska, P., 2010. Supply Chain Arrangements in Recovery Network, in: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J.,Jain, L.C. (Eds.), Agent and Multi-Agent Systems: Technologies and Applications: 4th KES International Symposium, KES- AMSTA 2010, Gdynia, Poland, June 23-25, 2010, Proceedings. Part II. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 292-301.

Keh, P., Rodhain, F., Meissonier, R., Llorca, V., 2012. Financial Performance, Environmental Compliance, and Social Outcomes: The three Challenges of Reverse Logistics. Case Study of IBM Montpellier. Supply Chain Forum: An International Journal 13 (3), 26–38.

Kilic, H.S., Cebeci, U., Ayhan, M.B., 2015. Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey. Resources, Conservation and Recycling 95, 120–132.

Kim, H., Yang, J., Lee, K.-D., 2009. Vehicle routing in reverse logistics for recycling end- of-life consumer electronic goods in South Korea. Transportation Research Part D 14 (5), 291–299.

Kissling, R., Fitzpatrick, C., Boeni, H., Luepschen, C., Andrew, S., Dickenson, J., 2012. Definition of generic re-use operating models for electrical and electronic equipment. Resources, Conservation and Recycling 65, 85–99.

Krikke, H., 2011. Impact of closed-loop network configurations on carbon footprints: A case study in copiers. Resources, Conservation and Recycling 55 (12), 1196–1205.

Krikke, H., Bloemhof-Ruwaard, J., Van Wassenhove, L., 2003. Concurrent product and closed-loop supply chain design with an application to refrigerators. International Journal of Production Research 41 (16), 3689–3719.

Krikke, H.R., Van Harten, A., Schuur, P., 1999. Business case Roteb: recovery strategies for monitors. Computers & Industrial Engineering 36 (4), 739–757.

Krumwiede, D.W., Sheu, C., 2002. A model for reverse logistics entry by third-party providers. Omega 30 (5), 325–333.

Kumar, A., Holuszko, M., Espinosa, D.C.R., 2017. E-waste: An overview on generation,

collection, legislation and recycling practices. Resources, Conservation and Recycling 122, 32–42.

Landers, T.L., Cole, M.H., Walker, B., Kirk, R.W., 2000. The virtual warehousing concept. Transportation Research Part E: Logistics and Transportation Review 36 (2), 115–126.

Lee, D.-H., Dong, M., 2008. A heuristic approach to logistics network design for end-of- lease computer products recovery. Transportation Research Part E: Logistics and Transportation Review 44 (3), 455–474.

Lee, T.-R., Chang, H.-Y., Chen, S.-Y., 2007. An investigation of perception gap of reverse logistics service quality: the case of mobile phone industry. International Journal of Global Environmental issues 7 (1), 25–52.

Lei, L., Qu, L., 2011. The construction of information platform about WEEE reverse lo- gistic based on virtual symbiotic network. 6–8 May 2011. Paper presented at the 2011 International Conference on E-Business and E-Government (ICEE).

Li, J., Tian, B., Liu, T., Liu, H., Wen, X., Honda, S.i., 2006. Status quo of e-waste man- agement in mainland China. Journal of Material Cycles and Waste Management 8 (1), 13–20.

Li, X., Li, Y.-j., Cai, X.-q., 2009. Collection Pricing Decision in a Remanufacturing System Considering Random Yield and Random Demand. Systems Engineering - Theory & Practice 29 (8), 19–27.

Li, X., Wu, Q., Li, J., Zhu, D., 2010. A Coordination Mechanism in E-Waste Reverse Logistics. 24–26 Aug. 2010. Paper presented at the 2010 International Conference on Management and Service Science.

Linton, J.D., Johnston, D.A., 2000. A decision support system for planning re- manufacturing at Nortel Networks. Interfaces 30 (6), 17–31.

Liu, H., Lei, M., Deng, H., Keong Leong, G., Huang, T., 2016. A dual channel, quality- based price competition model for the WEEE recycling market with government subsidy. Omega 59 (Part B), 290–302.

Liu, J., Zhong, H., Wei, W., 2010. Composition and evaluation of Waste Electric and Electronic Equipment reverse logistics capability. 7–10 Dec. 2010. Paper presented at the 2010 IEEE International Conference on Industrial Engineering and Engineering Management.

Liu, Y., Zhang, Y.F., Jin, Y.X., 2014. Reverse logistics network design of waste electrical appliances. Paper presented at the Applied Mechanics and Materials.

Machado, V.H., Barroso, A.P., Barros, A.R., Machado, V.C., 2010. Waste Electrical and Electronic Equipment Management. A case study. 7–10 Dec. 2010. Paper presented at the 2010 IEEE International Conference on Industrial Engineering and Engineering Management.

Machi, L.A., McEvoy, B.T., 2016. The literature review: Six steps to success. Corwin Press. Mahmoudzadeh, M., Mansour, S., Karimi, B., 2013. To develop a third-party reverse lo-

gistics network for end-of-life vehicles in Iran. Resources, Conservation and Recycling 78, 1–14.

Manzini, R., Bortolini, M., Ferrari, E., Piergallini, A., 2011. A supporting decision tool for reverse logistics. Paper presented at the 21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings.

Mar-Ortiz, J., Adenso-Diaz, B., González-Velarde, J.L., 2011. Design of a recovery net- work for WEEE collection: the case of Galicia. Spain. Journal of the Operational Research Society 62 (8), 1471–1484.

Mar-Ortiz, J., González-Velarde, J.L., Adenso-Díaz, B., 2012. Reverse Logistics Models and Algorithms: Optimizing WEEE Recovery Systems. Computación y Sistemas 16 (4), 491–496.

Mar-Ortiz, J., González-Velarde, J.L., Adenso-Díaz, B., 2013. Designing routes for WEEE collection: the vehicle routing problem with split loads and date windows. Journal of Heuristics 19 (2), 103–127.

Maslennikova, I., Foley, D., 2000. Xerox’s approach to sustainability. Interfaces 30 (3), 226–233.

Mata-Lima, H., Alvino-Borba, A., Pinheiro, A., Mata-Lima, A., Almeida, J.A., 2013. Impactos dos desastres naturais nos sistemas ambiental e socioeconômico: o que faz a diferença? Ambiente & Sociedade 16, 45–64.

Mayring, P., 2001. Combination and integration of qualitative and quantitative analysis. Paper presented at the Forum Qualitative Sozialforschung/Forum: Qualitative Social Research.

Mayring, P., 2014. Qualitative content analysis: theoretical foundation, basic procedures and software solution.

Mazhar, M.I., Kara, S., Kaebernick, H., 2007. Remaining life estimation of used compo- nents in consumer products: Life cycle data analysis by Weibull and artificial neural networks. Journal of Operations Management 25 (6), 1184–1193.

Meredith, J., 1993. Theory building through conceptual methods. International Journal of Operations & Production Management 13 (5), 3–11.

Miles, M.B., Huberman, A.M., 1994. Qualitative data analysis: An expanded sourcebook. sage.

Mont, O., 2000. Product-Service Systems. The International Institute of Industrial Environmental Economics, Lund University, Stockholm, Sweden.

Moussiopoulos, N., Karagiannidis, A., Papadopoulos, A., Achillas, C., Antonopoulos, I., Perkoulidis, G., Vlachos, D., Vlachokostas, C., 2012. Transportation cost analysis of the Hellenic system for alternative management of Waste Electrical and Electronic Equipment. International Journal of Environment and Waste Management 10 (1), 70–89.

Mukhopadhyay, S.K., Ma, H., 2009. Joint procurement and production decisions in re- manufacturing under quality and demand uncertainty. International Journal of Production Economics 120 (1), 5–17.

Nagel, C., Meyer, P., 1999. Caught between ecology and economy: end-of-life aspects of environmentally conscious manufacturing. Computers & Industrial Engineering 36 (4), 781–792.

Nagurney, A., Toyasaki, F., 2005. Reverse supply chain management and electronic waste

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0350

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0355

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0360

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0365

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0370

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0375

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0380

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0390

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0395

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0400

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0405

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0410

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0415

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0420

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0425

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0430

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0435

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0440

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0445

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0450

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0460

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0465

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0470

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0475

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0480

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0485

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0490

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0495

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0500

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0505

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0510

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0515

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0520

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0525

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0530

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0535

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0540

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0545

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0550

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0555

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0560

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0565

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0570

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0575

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0580

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0585

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0590

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0595

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0600

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0605

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0610

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0615

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0620

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0625

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0630

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0635

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0640

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0645

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0650

recycling: a multitiered network equilibrium framework for e-cycling. Transportation Research Part E 41 (1), 1–28.

Nenes, G., Nikolaidis, Y., 2012. A multi-period model for managing used product returns. International Journal of Production Research 50 (5), 1360–1376.

Nnorom, I.C., Osibanjo, O., 2008. Electronic waste (e-waste): Material flows and man- agement practices in Nigeria. Waste Management 28 (8), 1472–1479.

Nobre, G.C., Tavares, E., 2017. Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study. Scientometrics 111 (1), 463–492.

OECD, 2017. Extended producer responsibility. (Accessed September 16 2017). http:// www.oecd.org/env/tools-evaluation/extendedproducerresponsibility.htm.

Parlikad, A.K., McFarlane, D., 2007. RFID-based product information in end-of-life de- cision making. Control Engineering Practice 15 (11), 1348–1363.

Pérez-Belis, V., Bovea, M., Ibáñez-Forés, V., 2015. An in-depth literature review of the waste electrical and electronic equipment context: Trends and evolution. Waste Management & Research 33 (1), 3–29.

Pimentel, M., Rocha, J., Rocha, T.B., Moraes, D., 2013. Ambientronic: a Brazilian pro- gram to support the development of innovative projects in e-waste reverse logistics. Paper presented at the Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems.

Piplani, R., Saraswat, A., 2012. Robust optimisation approach to the design of service networks for reverse logistics. International Journal of Production Research 50 (5), 1424–1437.

Ponce-Cueto, E., Manteca, J.Á.G., Carrasco-Gallego, R., 2011. Reverse Logistics for Used Portable Batteries in Spain: An Analytical Proposal for Collecting Batteries. In: Golinska, P., Fertsch, M., Marx-Gómez, J. (Eds.), Information Technologies in Environmental Engineering: New Trends and Challenges. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 593–604.

Potter, A., Childerhouse, P., Coronado Mondragon, A.E., Lalwani, C., Coronado Mondragon, C.E., 2011. Measures for auditing performance and integration in closed- loop supply chains. Supply Chain Management: An International Journal 16 (1), 43–56.

Qiang, Q., Ke, K., Anderson, T., Dong, J., 2013. The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega 41 (2), 186–194.

Qiang, S., Zhou, X.-Z., 2016. Robust reverse logistics network design for the waste of electrical and electronic equipment (WEEE) under recovery uncertainty. Journal of Environmental Biology 37 (5), 1153–1165.

Queiruga, D., Walther, G., González-Benito, J., Spengler, T., 2008. Evaluation of sites for the location of WEEE recycling plants in Spain. Waste Management 28 (1), 181–190.

Ravi, V., Shankar, R., Tiwari, M., 2005a. Productivity improvement of a computer hardware supply chain. International Journal of Productivity and Performance Management 54 (4), 239–255.

Ravi, V., Shankar, R., Tiwari, M., 2008. Selection of a reverse logistics project for end-of- life computers: ANP and goal programing approach. International Journal of Production Research 46 (17), 4849–4870.

Ravi, V., Shankar, R., Tiwari, M.K., 2005b. Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach. Computers & Industrial Engineering 48 (2), 327–356.

Robotis, A., Bhattacharya, S., Van Wassenhove, L.N., 2005. The effect of remanufacturing on procurement decisions for resellers in secondary markets. European Journal of Operational Research 163 (3), 688–705.

Rousis, K., Moustakas, K., Malamis, S., Papadopoulos, A., Loizidou, M., 2008. Multi-cri- teria analysis for the determination of the best WEEE management scenario in Cyprus. Waste Management 28 (10), 1941–1954.

Sabtu, M.I., Saibani, N., Ramli, R., Ab Rahman, M.N., 2015. Multi-criteria decision making for reverse logistic contractor selection in e-waste recycling industry using polytomous rasch model. Jurnal Teknologi 77 (27), 119–125.

Salema, M.I.G., Barbosa-Povoa, A.P., Novais, A.Q., 2007. An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European Journal of Operational Research 179 (3), 1063–1077.

Sasikumar, P., Haq, A.N., 2011. Integration of closed loop distribution supply chain network and 3PRLP selection for the case of battery recycling. International Journal of Production Research 49 (11), 3363–3385.

Savaskan, R.C., Van Wassenhove, L.N., 2006. Reverse channel design: the case of com- peting retailers. Management Science 52 (1), 1–14.

Schultmann, F., Engels, B., Rentz, O., 2003. Closed-Loop Supply Chains for Spent Batteries. Interfaces 33 (6), 57–71.

Seuring, S., Gold, S., 2012. Conducting content-analysis based literature reviews in supply chain management. Supply Chain Management: An International Journal 17 (5), 544–555.

Seuring, S., Müller, M., Westhaus, M., Morana, R., 2005. Conducting a literature review- the example of sustainability in supply chains. Research Methodologies in Supply Chain Management 91–106.

Shanshan, W., Kejing, Z., 2008. Optimization model of e-waste reverse logistics and re- cycling network. Paper presented at the Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on.

Sharma, M., Ammons, J.C., Hartman, J.C., 2007. Asset management with reverse product flows and environmental considerations. Computers & Operations Research 34 (2), 464–486.

Shi, X., Li, L.X., Yang, L., Li, Z., Choi, J.Y., 2012. Information flow in reverse logistics: an industrial information integration study. Information Technology and Management 13 (4), 217–232.

Shih, H.-S., Stanley Lee, E., Chuang, S.-H., Chen, C.-C., 2012. A forecasting decision on the sales volume of printers in Taiwan: An exploitation of the Analytic Network Process. Computers & Mathematics with Applications 64 (6), 1545–1556.

Shih, L.-H., 2001. Reverse logistics system planning for recycling electrical appliances and computers in Taiwan. Resources, Conservation and Recycling 32 (1), 55–72.

Shokohyar, S., Mansour, S., 2013. Simulation-based optimisation of a sustainable re- covery network for Waste from Electrical and Electronic Equipment (WEEE). International Journal of Computer Integrated Manufacturing 26 (6), 487–503.

Shokohyar, S., Mansour, S., Karimi, B., 2013. Simulation-based optimization of ecological leasing: a step toward extended producer responsibility (EPR). The International Journal of Advanced Manufacturing Technology 66 (1), 159–169.

Shokohyar, S., Mansour, S., Karimi, B., 2014. A model for integrating services and pro- duct EOL management in sustainable product service system (S-PSS). Journal of Intelligent Manufacturing 25 (3), 427–440.

Shokouhyar, S., Aalirezaei, A., 2017. Designing a sustainable recovery network for waste from electrical and electronic equipment using a genetic algorithm. International Journal of Environment and Sustainable Development 16 (1), 60–79.

Shumon, M.R.H., Ahmed, S., Islam, M.T., 2014. Electronic waste: present status and fu- ture perspectives of sustainable management practices in Malaysia. Environmental Earth Sciences 72 (7), 2239–2249.

Sodhi, M.S., Reimer, B., 2001. Models for recycling electronics end-of-life products. OR Spectrum 23 (1), 97–115.

Srivastava, S.K., 2008a. Network design for reverse logistics. Omega 36 (4), 535–548. Srivastava, S.K., 2008b. Value recovery network design for product returns. International

Journal of Physical Distribution & Logistics Management 38 (4), 311–331. Stevens, G.C., 1989. Integrating the supply chain. International Journal of Physical

Distribution & Materials Management 19 (8), 3–8. Stock, J.R., 1992. Reverse logistics: White paper. Council of Logistics Management. Subramanian, R., Ferguson, M.E., Beril Toktay, L., 2013. Remanufacturing and the

component commonality decision. Production and Operations Management 22 (1), 36–53.

SWICO, 2017. Technical Report 2017. SWICO Recycling. Tan, A., Kumar, A., 2008. A decision making model to maximise the value of reverse

logistics in the computer industry. International Journal of Logistics Systems and Management 4 (3), 297–312.

Tari, I., Alumur, S.A., 2014. Collection center location with equity considerations in re- verse logistics networks. INFOR: Information Systems and Operational Research 52 (4), 157–173.

Temur, G.T., Kaya, T., Kahraman, C., 2014. Facility location selection in reverse logistics using a type-2 fuzzy decision aid method. Supply Chain Management Under Fuzziness. Springer, pp. 591–606.

Tonanont, A., Yimsiri, S., Jitpitaklert, W., Rogers, K., 2008. Performance evaluation in reverse logistics with data envelopment analysis. Paper presented at the IIE Annual Conference.

Tuzkaya, G., Gülsün, B., Önsel, Ş., 2011. A methodology for the strategic design of reverse logistics networks and its application in the Turkish white goods industry. International Journal of Production Research 49 (15), 4543–4571.

Van Wassenhove, L.N., Zikopoulos, C., 2010. On the effect of quality overestimation in remanufacturing. International Journal of Production Research 48 (18), 5263–5280.

Vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A., 2009. Reconstructing the giant: On the importance of rigour in documenting the literature search process. Paper presented at the ECIS.

Wadhwa, S., Madaan, J., Chan, F.T.S., 2009. Flexible decision modeling of reverse lo- gistics system: A value adding MCDM approach for alternative selection. Robotics and Computer-Integrated Manufacturing 25 (2), 460–469.

Walther, G., Schmid, E., Spengler, T.S., 2008. Negotiation-based coordination in product recovery networks. International Journal of Production Economics 111 (2), 334–350.

Wang, I.L., Yang, W.C., 2007. Fast Heuristics for Designing Integrated E-Waste Reverse Logistics Networks. IEEE Transactions on Electronics Packaging Manufacturing 30 (2), 147–154.

Wang, X., Zhang, K., Yang, B., 2011. Optimal design of reverse logistics network on e- waste in Shanghai. International Journal of Networking and Virtual Organisations 8 (3-4), 209–223.

Wang, Z.-H., Yin, J.-H., Ma, W.-M., 2008. A fuzzy modeling and solution algorithm for optimization on E-waste reverse logistics. Paper presented at the International Conference on Machine Learning and Cybernetics.

Wee Kwan Tan, A., Kumar, A., 2006. A decision-making model for reverse logistics in the computer industry. The International Journal of Logistics Management 17 (3), 331–354.

Wee Kwan Tan, A., Shin Yu, W., Arun, K., 2003. Improving the performance of a com- puter company in supporting its reverse logistics operations in the Asia-Pacific re- gion. International Journal of Physical Distribution & Logistics Management 33 (1), 59–74.

Wei, J., Zhao, J., 2013. Reverse channel decisions for a fuzzy closed-loop supply chain. Applied Mathematical Modelling 37 (3), 1502–1513.

Xanthopoulos, A., Iakovou, E., 2009. On the optimal design of the disassembly and re- covery processes. Waste Management 29 (5), 1702–1711.

Xianfeng, L., Jianwei, Q., Meilian, L., 2010. Design and simulation WEEE reverse logistics network in Guangxi. Paper presented at the Optoelectronics and Image Processing (ICOIP), 2010 International Conference on.

Xie, Q.H., Zhang, X.W., Lv, W.G., Cheng, S.Y., Huang, H.X., Cai, S.D., 2013. Research on re-use reverse logistics network of disused electric appliances based on election campaign algorithm. Paper presented at the Applied Mechanics and Materials.

Xu, Z., 2008. Integrated information systems of e-waste take-back supply chain. Paper presented at the Wireless Communications, Networking and Mobile Computing, 2008. WiCOM'08. 4th International Conference on.

Xu, Z., Elomri, A., Pokharel, S., Zhang, Q., Ming, X.G., Liu, W., 2017. Global reverse supply chain design for solid waste recycling under uncertainties and carbon emis- sion constraint. Waste Management 64, 358–370.

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0650

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0655

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0660

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0665

http://www.oecd.org/env/tools-evaluation/extendedproducerresponsibility.htm

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0675

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0680

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0685

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0690

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0695

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0700

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0705

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0710

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0715

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0720

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0725

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0730

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0735

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0740

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0745

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0750

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0755

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0760

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0765

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0770

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0775

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0780

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0785

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0790

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0795

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0800

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0805

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0810

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0815

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0820

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0825

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0830

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0835

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0840

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0845

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0850

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0855

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0860

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0865

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0870

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0875

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0880

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0885

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0890

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0895

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0900

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0905

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0910

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0915

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0920

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0925

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0930

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0935

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0940

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0945

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0950

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0955

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0960

Xuping, W., Zilai, S., Jun, Z., 2013. A study on Principal-agent Relationship for Third- party Reverse Logistics Based on EPR. Paper presented at the Intelligent Decision Technologies: Proceedings of the 5th KES International Conference on Intelligent Decision Technologies (KES-IDT 2013).

Yu, H., Solvang, W.D., 2013. A reverse logistics network design model for sustainable treatment of multi-sourced Waste of Electrical and Electronic Equipment (WEEE). 2–5 Dec. 2013. Paper presented at the 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom).

Yu, H., Solvang, W.D., 2016. A Stochastic Programming Approach with Improved Multi- Criteria Scenario-Based Solution Method for Sustainable Reverse Logistics Design of Waste Electrical and Electronic Equipment (WEEE). Sustainability 8 (12), 1–28.

Yuksel, H., 2009. An Analytical Hierarchy Process decision model for e-waste collection center location selection. In: Industrial Engineering. 6–9 July, 2009. Paper presented at the 2009 International Conference on Computers &.

Zhang, H.C., Li, J., Shrivastava, P., Whitley, A., Merchant, M.E., 2004. A web-based system for reverse manufacturing and product environmental impact assessment considering end-of-life dispositions. CIRP Annals - Manufacturing Technology 53 (1), 5–8.

Zikopoulos, C., Tagaras, G., 2007. Impact of uncertainty in the quality of returns on the profitability of a single-period refurbishing operation. European Journal of Operational Research 182 (1), 205–225.

M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0965

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0970

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0975

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0980

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0985

http://refhub.elsevier.com/S0921-3449(18)30201-5/sbref0990