**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__

__Operations Research: Applications and Algorithms__by Wayne L. Winston.__Applied Mathematical Programming__by Stephen P. Bradley, Arnoldo C. Hax and Thomas L. Magnanti.__Model Building in Mathematical Programming__by H. Paul Williams.__Introduction to Linear Optimization__by Dimitris Bertsimas and John N. Tsitsiklis.__Linear Programming__by Robert J. Vanderbei.

**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.

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