Prerequisites
Graduate standing or an undergraduate course on probability and/or statistics. Typical courses include, but are not limited to, ME EN 2550, CS3130, ECE3530, or MATH 3070.
Student Learning Objectives
Upon successful completion of this course, students shall be able to:
- Model optimization problems and solve with software.
- Solve linear programs with the simplex method.
- Understand and apply sensitivity analysis.
- Implement methods to solve nonlinear and integer programs (branch and bound, gradient search, one dimensional search)
- Model and solve discrete and continuous stochastic systems
Course Description
This course provides a broad overview of operations research topics with a focus on finding mathematically optimal solutions for systems. An emphasis is placed upon real world applications. Topics covered include: linear programming, integer programming, nonlinear programming, discrete Markov chains and queueing theory.
Sample Lectures
This lecture describes one iteration of the simplex method. It is only one of many such lectures on the simples method in the class.
The following lecture describes modeling of the transportation linear program.
The next three lecture describe how to build and solve the transportation model in Python by importing PULP. Three of the course projects build linear or integer programs for various systems and use Python and PULP to solve and analyze the solutions. The class assumes that students have had no experience with Python. A brief primer is presented in a prior lecture. Between the python primer, lectures involving modeling and python, and instructor or TA help, the vast majority of students are able to leave the class with skills to model and solve linear and integer programs in Python.