Operations Research

3 Credits · Level: Graduate, Undergraduate

This is a MS level course covering the foundations of operations research with emphasis on mathematical modeling, linear optimization, and metaheuristics. Linear optimization (or linear programming, LP) is a fundamental branch of optimization, with applications to several areas such as physical sciences, health care, manufacturing, logistics, computer science, and finance. This course will provide an integrated view of the theory, solution techniques and applications of LP. Metaheuristics are algorithms that can be used to find quality solutions to a variety of optimization problems, where analytic solutions are impractical. There are many classic examples of optimization via LP. The diet problem takes a set of foods that have varying nutrient levels and costs. The constraint would be in the form of a recommended daily minimum nutritional intake, and the problem is to satisfy the nutritional goals at minimum cost. A background in basic Python, linear algebra, and multivariate calculus is assumed. Topics covered include mathematical modeling, LP formulation, the simplex method, sensitivity analysis, network flows, simulated annealing, and genetic algorithms. Mathematical modeling software will be used throughout the course for solving examples.