Teaching



As instructor at Southern Methodist University

Operations Research (UG)

OREM 3360

Spring 2024, 2023, 2022, 2019, 2018, 2017; Fall 2022, 2021.

The course is designed for undergraduate students in their sophomore and junior years pursuing a degree in Management Science. The course covers linear programming and network optimization models and solution methods. Students are exposed to various applications involving resource allocation, scheduling, transportation, production planning, supply chain management, etc.​

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

Syllabus: PDF (Fall 2023)

Advanced Operations Research (UG)

OREM 5364

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)

Optimization for Analytics (G)

OREM 8360

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)

Linear Programming (G)

OREM 8371

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

Stochastic Programming (G)

OREM 8384

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)


Past Courses

Instructor, University of Southern California

  • ISE 536: Linear Programming and Extensions (G)
    Fall 2014
  • ISE 330: Introduction to Operations Research – Deterministic Modeling (UG)
    Spring 2015, Fall 2014, Spring 2014 and Fall 2013
  • ISE 310: Production I: Facilities and Logistics
    Spring 2015
  • ISE 499: Special Topics – Integrative Systems Engineering (UG)
    Spring 2014 and Spring 2014

Teaching Associate, Ohio State University

  • ISE 599.01: Systems Modelling (UG)
    Spring 2012, Fall 2011 and Spring 2011
  • ENG 181.01: Introduction to Engineering I
    Winter 2010
  • ECE 209: Circuits and Electronics Laboratory
    Spring 2009
(UG: undergraduate; G: graduate)