Introduction

Introduction to Operations Research
Chapter 1: Introduction
Chapter 2: Overview of OR
Chapter 3: Introduction to Linear Programming
1. Dene the problem of interest and gather relevant data. (Users may not even understand their problem  you have to clarify it.)
2. Formulate a mathematical model. (Basically, choose decision variables x1, x2, . . . , xn, constraints on decision variables and objective functions of the decision variables to be optimized.)
3. Derive solution(s) from the model and do post-optimality analysis such as sensitivity of solutions, what-if s, etc.
4. Test the model. (Do answers make sense, can you test against prior data, are there blunders, mathematical or numerical instabilities?) If tests fail, go to 1.
5. Prepare to apply the model. (Develop a full, reliable, usable system for end users.)
6. Implementation. (Implement as prescribed by management.) In this course, we will primarily focus of items 14.
First a disclaimer: Here programming does not mean computer programming but rather, planning. So these models are called
linear programs.
Much of the work we do will be by hand. This would never be done in the real world, but our objectives include understanding the
methods at a higher level, and you have to get your hands dirty to do that.
Nonetheless, the algorithms that come from linear programming are very programmable, and we will use software that does the grunt
work for more detailed and realistic problems.