this is a group project .this student only take in charge of three parts : introduction , literature survey and background, must follow the topic I attached below

Problem Statement:

Alarmed by the gradual aging of the population and the current hospital bed deficits in certain developed areas, we are determined to excavate the seriousness of this worldly issue using prediction and optimization models. In other words, what caused the problem, how serious is it, and why? What we are trying to do is to seek out how the population aging drags down the stagnating economy, which in turn worsens the hospital bed deficits, and eventually find a feasible resolution to make the current hospital bed arrangement system allocatively efficient.

Mathematical Modeling:

The database that we use is medicare.gov, a trustworthy federal government website run and managed by The Centers for Medicare & Medicaid Services located in Baltimore, MD. To analyze the data we acquired from the database, we will be utilizing microeconomic/macroeconomic tools and mathematical models to predict and find the best suitable solution to the situation. The challenge of the research will be finding an optimal solution that takes in all factors and parties into account, without hurting any affiliated goods and services, or even the entire economy. In the field of this particular research, the best models at hand are Linear Regression/Optimization model, Logistics model, and the Erlang Loss Formula.

Variables:

Variable Symbol	Variable Name
F	Patient Flow
A	Patient Age
PA	Population Age (Mean)
N	Number of Nurses
D	Number of Doctors
S	Number of Other Staff Members
H	Hospital Departments
B	Maximum Number of Beds Available
E	Economy
W	Local Wealth Base
U%	Unemployment Rate
R	Daily Rotations of Nurses
SA	Allocation System of Hospital Beds

Important Notations:

Parameter Symbol	Parameter Name
	Opportunity Cost of reallocation of nurses in hospital
	Opportunity/Foregone Cost of reallocating doctors in hospital
	Opportunity Cost of reallocation of other staff members in hospital
	Opportunity/Economic Cost of rearranging hospital beds in hospital
	The maximum capacity of patients in hospital
	Maximum number of beds can be allocated in hospital
	Maximum number of doctors can be allocated in hospital without losing function
	Maximum number of nurses can be allocated in hospital without losing function
	Maximum number of other staff members can be allocated in hospital without losing function
	Maximum Full-employment Output of Economy in country
	Maximum Fund Available in Medicare Sector in country
	Local Incentive to Invest in Medicare in area
	Opportunity/Economic Cost of Training new doctors in country
	Opportunity/Economic Cost of Training new nurses in country
	Opportunity/Economic Cost of Training new medicare-affiliated personnel in country

Decision Variables:

List of Formulae & Models:

a) Aging of population and Economy:

· Gini Coefficient

· Lorenz Curve

· Phillips Curve

· Demand & Supply Curve for the Market in Hospital Beds

b) Reallocation of Hospital Beds based on a):

· Logistics Model for the prediction of future patient flow

· Convolutional Neural Network to help level up the efficiency of Logistics Model by adding artificial intelligence training components into it

· Linear Regression

· Erlang Loss Formula A&B&C

· Network Furl Model

· Linear Optimization Model