Chapter 15 Summary
An Introduction to Optimization Models and Applications in Healthcare Delivery Systems

Chapter Authors
Wenhua Cao - University of Houston
Gino J. Lim - University of Houston

Some Commentary

This chapter gets into a fair bit of mathematical modelling. I’m not the right person to teach that, and will skip over those sections; therefore, this will be a very brief summary.

1. Optimization

The chapter sources the early days of “optimization” to World War II military logistics. It involves maximizing or minimizing a set of variables, given particular constraints.

The process of optimization has three components: (1) modelling the problem, (2) solving the model, (3) post-optimization analysis.

2.  Optimization in Healthcare

An example is the nurse shift optimization problem. It involves the competing goals of “maximizing the service level, minimizing the cost, and maximizing the nurse preferences of shifts, and so on”

Other examples listed include

• medical waste collection
• medical facility locations
• medical facility capacity planning
• inpatient and outpatient patient scheduling
• disease prediction
• radiotherapy planning

3.  Mathematical Models

The text discusses the following models

• linear programing models

• mixed integer programming models
• nonlinear programming models
• stochastic programming models
• multi-objective optimization
• Solution Algorithms: simplex method; branch and bound: meta-heuristic local search,  simulated annealing, genetic algorithm

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