OPERATIONS RESEARCH

Catalog description

This course provides a comprehensive introduction to the principles, models and methods of operations research for analyzing and solving complex decision-making problems. It covers both deterministic and stochastic approaches, beginning with linear programming, integer programming, network optimization and nonlinear programming, and extending to probabilistic models that address uncertainty for real-world systems. Students learn to apply structured modeling techniques, decision-making frameworks and state-of-the-art optimization solvers to analyze systems that exhibit nonlinear and probabilistic behavior. Topics include decision making under uncertainty, game theory, Markov chains, queuing theory and dynamic programming. Emphasis is placed on developing practical modeling skills and the ability to interpret results for engineering and organizational applications rather than formal mathematical rigor.