LP6

Linear Programming Sensitivity Analysis

Recall the Acme Bicycle Company example:

Variables:

  • x₁: mountain bikes to produce (bikes/day)
  • x₂: racers to produce  (bikes/day)

Variable non-negativity:

  • x₁ ≥ 0,  x₂ ≥ 0

Objective Function:

  • Maximize daily profit ($/day):  Max Z =  15 x₁ + 10 x₂

Constraints:

  • Mountain bike production limit (bikes/day): x₁ ≤ 2
  • Racer production limit (bikes/day): x₂ ≤ 3
  • Metal finishing machine production limit (bikes/day): x₁ + x₂ ≤ 4

We will look at the sensitivity of the solution that we found in the previous examples. We will be answering this question:  is the optimum solution sensitive to a small change in one of the original problem coefficients?

Before the animation, look over the following output obtained from the LINDO LP solver. The animation will discuss the ranges boxed in red in particular. 






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Contents

Chapter 1: Introduction.  An introduction to the process of optimization and an overview of the major topics covered in the course.  Last revision: December 2020 . Chapter 2: Introduction to Linear Programming.  The basic notions of linear programming and the simplex method. The simplex method is the easiest way to provide a beginner with a solid understanding of linear programming.  Last revision: December 2020. See animation  LP1 . A good  article on formulating LPs  by Gerry Brown and Rob Dell. Example questions with solutions . March 4, 2021. Chapter 3: Towards the Simplex Method for Efficient Solution of Linear Programs.  Cornerpoints and bases. Moving to improved solutions.  Last revision: December 2020 . See animation  LP2 . Chapter 4: The Mechanics of the Simplex Method.  The tableau representation as a way of illustrating the process of the simplex method. Special cases such as degeneracy and unboundedness.  Last revision: December 2020. See animation  LP3 . Example questions
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