Teaching
KAIST
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.
University of South Florida
ESI 4312 Deterministic Operations Research
(Fall 2017, 2018, 2019, 2020 / Undergraduate Level)
This course covers basic techniques for modeling and optimizing deterministic systems with emphasis on linear programming, network optimization, basic mixed integer programming, and nonlinear programming. Students learn how to compute solutions of various optimization problems. Applications to production, logistics, and service systems are discussed. This course uses the Julia Language.
ESI 6491 Linear Programming and Network Optimization
(Fall 2015, 2016, 2017, 2018, 2019, 2020 / Ph.D. Level)
This course will be an intensive study of Linear Programming (LP) and Network Optimization problems. LP deals with the problem of minimizing or maximizing a linear function in the presence of linear equality and/or inequality constraints. Both the general theory and characteristics of LP optimization problems as well as effective solution algorithms and applications will be addressed. The course is a good one for students who are planning to apply Operations Research (OR) tools in all areas of application in the public and private sectors including production or manufacturing problems, service/logistics related problems, and various problems involving network flows as well as to learn an optimization software tool. This course uses the Julia Language. More information.
EIN 6935 Nonlinear Optimization and Game Theory
(Spring 2016, 2018, 2020 / Ph.D. Level)
This course will be an intensive study of nonlinear optimization and Game Theory. The first part will focus on theory and algorithms of nonlinear optimization. Topics include convex analysis, optimality conditions, Lagrangian duality, and numerical methods for unconstrained and constrained optimization problems. The second part will apply theory and algorithms of nonlinear optimization to equilibrium problems that arise in management science, transportation science, regional science, and economics. Theory and algorithms of variational inequalities and complementarity problems are used to analyze and compute equilibria in connection with nonlinear optimization. Topics include Nash equilibrium and leader-follower games. This course uses the Julia Language. More information.
EIN 6934 Revenue Management and Pricing
(Spring 2019 / Ph.D. Level)
Revenue Management (RM) is a set of operational tools for generating more revenue with resource allocations and/or dynamic pricing. In this course, we will cover the fundamental concepts of RM, with mathematical models and algorithms, including capacity control, network capacity control, overbooking, dynamic pricing, customer choice modeling, pricing under competition, estimation and forecasting. By the end of this course, students will be able to understand the basic principles of RM, build mathematical models and suggest proper computational solution methods.
ESI 6934 Network Modeling, Design, and Optimization
(Spring 2017, 2021 / Ph.D. Level)
This course covers selected topics in mathematical models arising in 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 risk-averse routing, vehicle routing problems, network user equilibrium, road pricing and network design, location problems, and modeling drivers’ decision making processes, with applications in bike-sharing services, electric-vehicle charging, hazardous materials transportation, and congestion mitigation. This course will also introduce some computational tools available in the Julia Language.
ESI 6410 Optimization in Operations Research
(Spring 2021 / MSEM Level) This course uses the Julia Language.