MGTS5489 — Decision Making
This course equips graduate students with advanced modeling techniques, methods, and tools for problem-solving and decision-making, helping them develop the expertise required to navigate complex scenarios and become effective decisionmakers. The course covers topics such as advanced problem-solving principles, decision analysis under uncertainty (including 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, and advanced optimization methods. Graduate students will be expected to communicate insights derived from decision models and analyses through both written reports and oral presentations, tailored for both specialized and general audiences. pre-req: LSBE graduate candidate