MGTS4489 — Optimization & Decision Making
This course provides students with a broad range of modeling techniques, methods and tools used in problem solving and decision making to help them develop the competence and skills necessary to navigate challenging situations and become effective decision makers. Topics covered include principles for problem solving, decision analysis with uncertainty (e.g. multi-attribute utility models, decision trees, and Bayesian models), utility and game theory, linear and nonlinear programming, dynamic programming, distribution and network optimization models, Markov decision processes, advanced optimization, etc. Students are expected to communicate insights from the application of decision models and the analysis in written and oral formats appropriate for a general audience. pre-req: ECON 3020 and MGTS 3301 and LSBE candidate, no grad credit