OA4203 Mathematical Programming

Advanced topics in linear programming, large scale systems, the decomposition principle, additional algorithms, bounded variable techniques, linear fractional programming, formulation and solution procedures for problems in integer variables. Applications to capital budgeting, large scale distribution systems, weapon systems allocation and others.

Prerequisite

OA3201

Lecture Hours

4

Lab Hours

0

Course Learning Outcomes

Upon successful completion of this course, students will achieve the following learning outcomes:

• Know how to distinguish between various optimization modeling techniques which incorporate uncertainty.
• Be able to describe basic properties of stochastic linear programs, especially the properties which pertain to fixed recourse decisions.
• Have the ability to model real-world problems with chance constraints.
• Have the skills to quantify the difference between deterministic, stochastic, and perfect-knowledge situations with the Expected Value of Perfect Information and the Value of the Stochastic Solution.
• Be able to solve stochastic programs with the L-shaped method and the progressive hedging decomposition.
• Understand how to detect and utilize important problem structure in stochastic programs, such as network recourse.