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MIS470-Chp9_ModelBasedMultiCriteria.pdf

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Chapter 9: Model-Based Decision Making: Optimization and Multi-Criteria Systems

Learning Objectives • Understand the basic concepts of analytical decision

modeling

• Describe how prescriptive models interact with data and the user

• Understand some different, well-known model classes

• Describe the key issues of multi-criteria decision making

Opening Vignette…

Midwest ISO Saves Billions by Better Planning of Power Plant Operations and Capacity Planning

• Company background

• Problem description

• Proposed solution

• Results

• Answer & discuss the case questions...

Questions for the Opening Vignette

1. In what ways were the individual companies in Midwest ISO better off being part of MISO as opposed to operating independently?

2. The dispatch problem was solved with a linear programming method. Explain the need of such method in light of the problem discussed in the case.

3. What were the two main optimization algorithms used? Briefly explain the use of each algorithm.

Decision Support Systems Modeling

• DSS modeling (optimization & simulation) contribute to organizational success. Examples include: • Pillowtex (see ProModel, 2013),

• Fiat (see ProModel, 2006),

• Procter & Gamble (see Camm et al., 1997), and others.

• INFORMS publications such as Interfaces, ORMS Today, and Analytics magazine have plenty of such example cases

Application Case 9.1

Optimal Transport for ExxonMobil Downstream Through a DSS

Questions for Discussion 1. List three ways in which manual scheduling of ships

could result in more operational cost as compared to the tool developed.

2. In what other ways can ExxonMobil leverage the decision support tool developed to expand and optimize their other business operations?

3. What are some strategic decisions that could be made by decision makers using the tool developed?

Major Modeling Issues • Problem identification and environmental analysis (information

collection)

• Variable identification • Influence diagrams, cognitive maps

• Forecasting/predicting • More information leads to better prediction

• Multiple models: An MSS can include several models, each of which represents a different part of the decision-making problem • Categories of models >>>

• Model management – DBMS vs. MBDM

Categories of Models

Model Categories Static and Dynamic Models • Static Analysis

• Single snapshot of the situation

• Single interval

• Steady state

• Dynamic Analysis • Dynamic models

• Evaluate scenarios that change over time

• Time dependent

• Represents trends and patterns over time

• More realistic: Extends static models

Application Case 9.2

Optimal Transport for ExxonMobil Downstream Through a DSS

•Company

•Problem description

•Proposed solution

•Results 10

Model Categories Current Trends in Modeling

 Development of Model/Solution Libraries

 NEOS Server for Optimization, https://neos-server.org/neos.

 Resources link at informs.org, https://www.informs.org.

 Web-based modeling (optimization/simulation/…)

 Multidimensional analysis (modeling)

 Influence Diagrams

https://neos-server.org/neos

https://www.informs.org/

Structure of Mathematical Models for Decision Support

Decision Variables

Mathematical Relationships

Uncontrollable Variables

Result Variables

 Non-Quantitative Models (Qualitative)

 Quantitative Models: Mathematically links decision variables, uncontrollable variables, and result variables

Intermediate Variables

Multi-Criteria Decision Making with Pairwise Comparisons • Having more than one criterion makes decision-making process

complicated

• Usually some type of weighing algorithm is used to analyze such problems

• The Analytic Hierarchy Process • Developed by Thomas Saaty (1995, 1996)

• A very popular technique for MCDM

• Popular Tools - ExpertChoice.com

• Web-based Tools - Web-HIPRE (hipre.aalto.fi)

Application Case 9.6

U.S. HUD Saves the House by Using AHP for Selecting IT Projects

•Company

•Problem description

•Proposed solution

•Results

Tutorial - Applying AHP Using Web-HIPRE • Goal: select the most appropriate movie

• Identify some criteria for making this decision

• The main and sub-criteria for movie selection are • a. Genre: Action, Comedy, Sci-Fi, Romance

• b. Language: English, Hindi

• c. Day of Release: weekday, weekend

• d. User/Critics Rating: High, Average, Low

• Alternatives are the following current movies: • SkyFall, The Dark Knight Rises, The Dictator, Dabaang, Alien, and

DDL

Tutorial - Applying AHP Using Web-HIPRE • Step 1: define the goal, criteria, and alternatives

Web-HIBRE allows defining all of these and relationships within an easy-to-use Web-based interface.

Tutorial - Applying AHP Using Web-HIPRE • Step 2: the main criteria are then ranked as they

relate to the goal • A comparative ranking scale from 1 to 9 (with ascending

order of importance) is used

• The ranking is done using a Pairwise comparison procedure (i.e., divide-and-concur) between any two criteria for all combinations of twos

• The tool readily normalizes the rankings of each of the main criteria over one another to a scale ranging from 0 to 1 and then calculates the row averages to arrive at an overall importance rating ranging from 0 to 1

Tutorial - Applying AHP Using Web-HIPRE

Tutorial - Applying AHP Using Web-HIPRE • Step 3: All of the subcriteria related to each of the

main criteria are then ranked with their relative importance over one another

• Step 4: Each alternative is ranked with respect to all of the subcriteria that are linked with the alternatives in a similar fashion using the relative scale of 0–9; then the overall importance of each alternative is calculated

• Step 5: The final result are obtained from the composite priority analysis involving all the subcriteria and main criteria

Tutorial - Applying AHP Using Web-HIPRE

End of the Chapter

• Questions, comments