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END 626E - Advanced Topics in Industrial Engineering

Course Objectives

This graduate level course is designed to prepare students for research in the area of operations research & industrial engineering (ORIE). In particular, we focus on OR applications in health care (broadly defined). There are two major guiding principles for the course: (i) learn about various healthcare topics for potential future applications, and (ii) review/reinforce our understanding of OR methodologies and how they are used. There will also be opportunities for computer implementations of various solution algorithms, which will make you face and hopefully overcome issues in computing, ultimately helping you for a deeper understanding of the topics we study.

Course Description

The course is designed for graduate students, but it is also open to advanced undergraduates who are interested in pursuing graduate studies. Students are expected to have a solid mathematical background; there are no other strict prerequisites. However, knowledge of deterministic and stochastic optimization, statistics, probability, and stochastic processes will be helpful.

The course is structured around detailed reading and in-depth discussion of published/working research papers. Research articles will be distributed for each week. Students are required to read and take an active role in discussing the materials in the papers, instead of staying as silent observers. The topics will include, among others, organ transplantation, matching, hospital operations, and vaccine supply chains. For each topic discussed, each student should address the following questions:
1. What is the problem/issue addressed? Provide details about the problem being studied.
2. What is the model/framework used? Explain the model(s) used by building them in a step-by-step fashion, emphasizing the major assumptions.
3. What are the major results/insights generated by the model? These could be analytical or numerical. Prove important analytical results. Discuss important numerical results and how they are obtained.
4. Critique the model:
a) When do you expect the model to perform well? Poorly?
b) What are some extensions/improvements that could be made in the model/results?

Course Coordinator
Burhaneddin Sandıkçı
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