Date of Award
Doctor of Philosophy
Matthew E.H. Petering
Hamid K Seifoddini, Xiaohang Yue, Yi Hu, Wilkistar A Otieno
HEURISTIC, MIXED INTEGER LINEAR PROGRAMMING, PANDEMIC, UNIVERSITY COURSE PLANNING, UNIVERSITY COURSE SCHEDULING
This dissertation has two chapters. In Chapter 1, we introduce two optimizationproblems related to university course planning. In the student course planning problem (SCPP), a student needs to design a course plan that allows him/her to graduate in a timely manner. In the department course planning problem (DCPP), an academic department needs to decide which courses to offer during which semesters to facilitate students’ timely graduation. Mathematical models of these problems are developed, coded in C++, and solved with IBM ILOG CPLEX. Experiments on small, medium-sized, and large real-world and fictional problem instances show the utility of the math model. Chapter 2 is about university course scheduling during a pandemic. Most universities have responded to the COVID-19 pandemic by offering courses in three formats: (1) online, (2) hybrid (with online and in-person components), or (3) in-person. Option 1 discourages student interaction; option 2 has low classroom utilization; and option 3 poses health risks or is limited to small courses meeting in large rooms. We propose a new approach to course scheduling which allows more than one classroom to be assigned to the same course. Our method allows all courses—even the largest—to have a limited number of socially distanced, in-person meetings each semester in which all students in the course meet in multiple classrooms simultaneously. A math model and heuristic method are developed for implementation. Analyses of life-sized problem instances are promising.
Khamechian, Mohammad, "University Course Scheduling During a Pandemic and University Course Planning: Math Models and Heuristic Algorithms" (2022). Theses and Dissertations. 3021.
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