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LP Validation Process

The document discusses potential issues that can arise when building linear programming models, referred to as the "dark side of LP". It lists 24 different problems that may affect any LP application, such as a model having unbounded solutions, multiple optimal solutions, infeasible solutions, degeneracy, redundancy among constraints, and shadow prices having the wrong sign or interpretation. The decision-maker and analyst need to be aware of these potential deficiencies when modeling with linear programming.

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234 views2 pages

LP Validation Process

The document discusses potential issues that can arise when building linear programming models, referred to as the "dark side of LP". It lists 24 different problems that may affect any LP application, such as a model having unbounded solutions, multiple optimal solutions, infeasible solutions, degeneracy, redundancy among constraints, and shadow prices having the wrong sign or interpretation. The decision-maker and analyst need to be aware of these potential deficiencies when modeling with linear programming.

Uploaded by

ricardo izquierdo
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/260751708

Tools for Modeling Validation Process: The Dark


Side of LP
Dataset March 2014

CITATION

READS

24

1 author:
Dr. Hossein Arsham
Johns Hopkins University
148 PUBLICATIONS 637 CITATIONS
SEE PROFILE

Available from: Dr. Hossein Arsham


Retrieved on: 25 May 2016

Tools for Modeling Validation Process:


The Dark Side of LP
What can go wrong in the process of building a Linear Programming (LP) model?
Potential pitfalls exist which affect any LP application; therefore, the decisionmaker and the analyst should be cognizant of deficiencies of LP at the modeling
stage.

1. Introduction
2. Unboundedness
3. Multiple Optimal Solutions (Innumerable optimal solutions)
4. No Solution (Infeasible LP)
5. Degeneracy
6. Degeneracy and the Dual (shadow) Prices
7. Shadow Price Might Have Wrong Sign
8. Redundancy Among the Constraints
9. Identification of Unbounded Feasible Regions
10. LP with no Vertex
11. LP With Unbounded, and Multiple Bounded Optimal Solutions
12. On the Basic and Nonbasic Decision Variables
13. LP Without Any Interior, and Boundary Solutions
14. Optimal Solution Generated by One LP Package Is Not Obtainable by the
Other
15. Does the Optimal Simplex Tableau Give the Dual Solution?
16. Solution to an Integer LP May Not Be One of the Integer Vertices
17. Conversion to the Standard Form May Distort the Feasible Region
18. Removal of Equality Constraints by Substitution May Change the Problem
19. Misinterpretation of the Shadow Price
20. Is the Shadow Price Always Nonnegative?
21. Alternative Shadow Prices
22. The Cost Sensitivity Range by Grapical Method
23. More-for-less & Less-for-more Situations
24. JavaScript E-labs

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