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.

Announcements

  • 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)

    Assignments

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