IE 331 Operations Research: Optimization

IE 331 Operations Research: Optimization, Spring 2023

TTh 4:00 - 5:15 pm, E2-2 #B105

Instructor: Dabeen Lee (E2-2 #2109)
Email: dabeenl [at] kaist [dot] ac [dot] kr
Office hours: TBD

Teaching Assistant: Jaehyun Park (Email: jhpark [at] kaist [dot] ac [dot] kr) & Junyeop Kwon (Email: junyeopk [at] kaist [dot] ac [dot] kr)

Text: No required text, but the following materials are helpful.

Recommended textbooks and lecture notes

Syllabus (pdf)

Operations Research & Management Science (ORMS) refers to analytical and quantitative techniques that are used in decision-making processes for organizations (including businesses). This course will focus on mathematical optimization and mathematical programming, arguably the most fundamental tool used for quantitative decision-making. We will learn basic yet fundamental frameworks to formulate a decision-making problem as a mathematical optimization model, taking into account data, constraints, and objectives. Topics include, but are not limited to, introduction to linear programming, network flow models, integer programming, scheduling, and stochastic programming.


  • No class on 5/16 (Tuesday).
  • No classes on 5/25 and 5/30 due to business trips.
  • Video lectures 5/23 and 6/01.
  • Final exam is on 6/15 (Thursday).
  • Lecture notes

    1. Tue 2/28: Introduction (lecture note)
    2. Thu 3/02: Modeling decision-making problems as optimization problems, Introduction to linear programming (lecture note)
    3. Tue 3/07: Linearly representable functions, Representing optimization problems as linear programs (lecture note)
    4. Thu 3/09: Projection, Linearly representable functions II, Production planning with holding costs (lecture note)
    5. Tue 3/14: Linear programming standard form, History of linear programming (lecture note)
    6. Thu 3/16: Geometry of linear programming, Simplex method I: {introduction} (lecture note)
    7. Tue 3/21: Simplex method II: {two-phase method, detecting infeasibility and unboundedness} (lecture note)
    8. Thu 3/23: Simplex method III: {technical details}, Upper and lower bounds on linear programs (lecture note)
    9. Tue 3/28: Linear programming duality (lecture note)
    10. Thu 3/30: Solving large-scale linear programming models: row and column generation (lecture note)
    11. Tue 4/04: Introduction to network models (lecture note)
    12. Thu 4/06: Minimum cost flow model, Total Unimodularity (lecture note)
    13. Tue 4/11: Minimum cost flow model with outflow and inflow capacities, Shortest path problem (lecture note)
    14. Thu 4/13: Maximum st-flow, Bipartite matching (lecture note)
    15. Tue 4/25: Introduction to integer programming, Hardness of integer programming, Modeling categorical decisions, Facility location (lecture note)
    16. Thu 4/27: Modeling logical relationships with binary variables, Transportation problem with ramp-up costs (lecture note)
    17. Tue 5/02: Modeling disjunctive constraints, Production with economic feasibility, Facility location revisited (lecture note)
    18. Thu 5/04: Introduction to optimization under uncertainty, Scenarios, Risk measures (lecture note)
    19. Tue 5/09: Value at Risk (VaR), Conditional Value at Risk (CVaR) (lecture note)
    20. Thu 5/11: Newsvendor problem with various risk measures (lecture note)
    21. Thu 5/18: Two-stage facility location (lecture note)
    22. Tue 5/23: Two-stage optimization framework with various risk measures (lecture note)
    23. Thu 6/01: Multi-stage optimization models, Scenario trees (lecture note)
    24. Thu 6/08: Two-period investment planning via multi-stage optimization (lecture note)


    1. Assignment 1 (pdf)
    2. Assignment 2 (pdf)
    3. Assignment 3 (pdf)