Teaching

Spring 2024; Fall 2023, 2022.

The course covers advanced topics pertaining to formulations, solution methods and applications of integer programming, non-linear programming, dynamic programming, and optimization under uncertainty. The course will also cover methods in metaheuristics, and formulation and solutions procedures for problems arising in game theory. Students must enroll in a laboratory section where specialized software for modeling and solving will be introduced.

Textbook: Fred Hillier and Gerald Lieberman, “Introduction to Operations Research,” 11 edition.

Syllabus : PDF (Fall 2020)

Fall 2020, 2018, 2017, 2016; Spring 2021, 2020

Primarily, designed for the students pursuing a Master’s degree program in the Department of Engineering Management, Information, and Systems as the first course in Operations Research. Graduate students with minimal or no exposure to Operations Research/Management Science from other fields of study can also find this course useful. The course provides an overview of operations research models and optimization techniques. It will involve an introductory level discussion of linear programming, integer programming, sequential decision problems, and decision making under uncertainty. The students will also be exposed to computational solution approaches including MS-Excel solver and AMPL. Previously, the course was titled ``Operations Research Models."

Textbook : Hamdy A. Taha, Operations Research: An Introduction, Pearson, 10th edition, 2017, ISBN-13: 9780134444017.

Syllabus : PDF (Fall 2020)

Fall 2019, 2018; Spring 2022.

This is the first doctoral course in the field of optimization that serves as thefoundation for all subsequent courses in the broad area of mathematical programming. The course is intended for first-year Ph.D. students and advanced M.S. students who intend to pursue a doctoral degree. The course provides complete development of theoretical and computational aspects of linear programming (LP) with topics such as — linear programming formulations, simplex algorithm, optimality conditions, duality, practical computation, and applications.

Textbook: Dimitris Bertsimas and John N. Tsitsiklis, Introduction to Linear Optimization, Athena Scientific, 1997, ISBN-13: 978-1-886529-19-9.

Syllabus : PDF Spring 2022

Fall 2024; Spring 2020, 2018

Stochastic programming (SP) is a systematic framework for modeling optimization problems involving uncertain data and finding optimal decisions. With its roots in mathematical programming, SP draws upon tools and methods developed in many disciplines, including mathematics, computer science, and statistics. Over the years, SP has gained significant attention as the work-horse for problems arising in various application areas, such as finance, infrastructure systems, healthcare systems, industrial engineering, agriculture, and telecommunication networks. This course will provide an overview of the different modeling approaches and solution methods cutting across the different themes of SP (two- and multistage risk-neutral, risk-averse, chance-constrained, and distributionally robust optimization). The course project will involve a study of recent developments in the field and computer implementation of solution methods in SP.

Resources :

• John R. Birge and François Louveaux, Introduction to Stochastic Programming , Springer New York, tenth edition, 2011, ISBN: 978-1-4614-0236-7 (Online: 978-1-4614-0237-4).
• Alexander Shapiro, Darinka Dentcheva and Andrzej Ruszczyński, Lectures on Stochastic Programming: Modeling and Theory, MOS-SIAM Series on Optimization, second edition, 2014, ISBN: 978-1-611973-42-6.
• Stein W. Wallace and William T. Ziemba, Applications of Stochastic Programming, MOS-SIAM Series on Optimization, 2005, ISBN: 978-0-89871-555-2.

Syllabus : PDF (Fall 2024)