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A Note on Fixing Misbehaving Mathematical Programs: Post-Optimality Procedures and GAMS-Related Software

Published online by Cambridge University Press:  28 April 2015

Bruce A. McCarl*
Affiliation:
Department of Agricultural Economics, Texas A&M University

Abstract

Mathematical programming formulations can yield faulty answers. Models can be unbounded, infeasible, or optimal with unrealistic answers. This article presents techniques for theory-based discovery of the cause of faulty models. The approaches are demonstrated in the context of linear programming. They have been computerized and interfaced using the General Algebraic Modeling System (GAMS), and are distributed free of charge through new GAMS versions and an online web page.

Type
Articles
Copyright
Copyright © Southern Agricultural Economics Association 1998

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References

Andersen, E.D., and Andersen, K.D.. “Preserving in Linear Programming.” Mathematical Programming 71(1995):221-45.CrossRefGoogle Scholar
Bazarra, M.S., Jarvis, J.J., and Sherali, H.D.. Linear Programming and Network Flows. New York: John Wiley and Sons, 1990.Google Scholar
Brooke, A., Kendrick, D., and Meeraus, A.. GAMS: A User's Guide. Version 2.25. San Francisco: Scientific Press, 1993.Google Scholar
Chinneck, J.W.Feasibility and Viability.” In Recent Advances in Sensitivity Analysis and Parametric Programming, eds., Gal, T. and Greenberg, H.J., Chap. 14. Boston: Kluwer Academic Publishers, 1997.Google Scholar
Greenberg, H.J.A Bibliography for the Development of an Intelligent Mathematical Programming System.” Interactive Transactions in Operations Research and Management Sciences 1,1(1996). Online. Available HTTP: http://www-math.cudenver.edu/~hgreenbe/impsbib/impsbib.html.Google Scholar
Greenberg, H.J.A Computer-Assisted Analysis System for Mathematical Programming Models and Solutions: A User's Guide for Analysis. Boston: Kluwer Academic Publishers, 1993.CrossRefGoogle Scholar
Greenberg, H.J.How to Analyze the Results of Linear Programs, Part 4: Forcing Substructures.” Interfaces 24,1(January/February 1994):121-30.CrossRefGoogle Scholar
Hadley, G.Linear Programming. Reading MA: Addison-Wesley, 1962.Google Scholar
McCarl, B.A.GAMSCHK USER DOCUMENTATION: A System for Examining the Structure and Solution Properties of Linear Programming Problems Solved Using GAMS. Dept. of Agr. Econ., Texas A&M University, 1997. Distributed through web page. Online. Available HTTP: http://agrinet.tamu.edu/mccarl.Google Scholar
McCarl, B.A.Model Validation: An Overview with Some Emphasis on Risk Models.” Rev. Mktg. and Agr. Econ. 52,3(1984):153-73.Google Scholar
McCarl, B.A., and Apland, J.D.. “Validation of Linear Programming Models.” S. J. Agr. Econ. 18,2(1986):155-64.Google Scholar
McCarl, B.A., and Spreen, T.H.. Applied Mathematical Programming Using Algebraic Systems. Draft textbook, Dept. of Agr. Econ., Texas A&M University, 1997. Distributed through web page. Online. Available HTTP: http://agrinet.tamu.edu/mccarl.Google Scholar
Pannell, D.J.Introduction to Practical Linear Programming. New York: John Wiley and Sons, 1997.Google Scholar