MG-GY 6103 Management Science (Section A) Fall 2025
Department of Technology Management and Innovation
Course Description This course introduces students to data-driven modeling and mathematical optimization techniques for strategic/tactical/operational (long/medium/short-term) decision-making in a variety of business contexts. Students learn to transform. business improvement needs and data into an optimization framework, apply various modeling techniques including linear/mixed-integer/non-linear/multi-criteria/network modeling, and use industrial optimization solvers and generative AI tools for solving large-scale optimization problems. In the later part of the course, students learn modeling and optimizing complex dynamic systems under uncertainty using simulation techniques and industrial software. Throughout the course, students work on hands-on real-world optimization projects, and read a number of cases on real-world implementations of large-scale optimization projects from various industries.
Course Prerequisites Basic programming, data manipulation in Python, algebra. The course assumes working knowledge of and data handling in Microsoft Excel and Python. Students who have not used Python for a while are directed to a self-learning crash course to brush it up in the first couple of weeks. Full instructions will be provided in class on Excel Add-ins and solvers as well as Python libraries used for optimization modeling.