Teaching
KAIST
IE 331 Operations Research: Optimization
This course intends to cover basic materials in the areas of operations research that prove to be most effective in real-world applications. Topics include Linear Programming, Integer Programming, Nonlinear Programming, Transportation, Network Model, and Dynamic Programming. Special emphasis is placed on solving the problems drawn from real-world situations and computational tools. By the end of this course, students will be able to derive a mathematical model for various decision-making problems and suggest a proper computational optimization method and a software tool.
IE 535 Network Theory and Applications
This graduate course focuses on mathematical optimization and equilibrium problems involving network systems arising in logistics, traffic management, telecommunication, urban science, spatial economics, etc. It covers a range of topics, starting with a review of linear programming and graph theory, and then delving into network optimization from both theoretical and algorithmic perspectives. The course further explores the characteristics of network user equilibrium, extending the study of nonlinear optimization theory. This graduate course serves as a foundation for students to conduct research in industrial engineering, operations research, transportation engineering, computer science, and applied economics, providing them with the skills to analyze, optimize, and design efficient network systems.
IE 801 Logistics Systems Optimization
This course covers selected topics in mathematical models for logistics network modeling, design, and optimization. We will briefly review basic topics in network optimization and then will proceed to commonly used models for logistics service planning by private companies as well as management of public network infrastructure, with emphasis on transportation systems. This course will cover topics such as the traveling salesman problem, vehicle routing problems, network design, and location problems. This course will introduce large-scale optimization algorithms such as Branch-and-Price, Branch-and-Cut, Branch-Cut-and-Price, Lagrangian Relaxation, Benders Decomposition, and Cutting Planes in the context of transportation and logistics systems.