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Solving Fuzzy Problems in Operations Research
James J. Buckley*, Thomas Feuring** and Yoichi Hayashi***
*Department of Mathematics, University of Alabama at Birmingham Birmingham, Alabama, 35294, USA
**Department of Electrical Engineering and Computer Science, University of Siegen H61derlinstr. 3, 57068 Siegen, Germany
***Department of Computer Science, Meiji University 1-1-1 Higashimita, Tama-ku, Kawasaki, 214-8571, Japan
Received:January 10, 1999Accepted:April 21, 1999Published:June 20, 1999
Keywords:Operations research, Fuzzy shortest route, Fuzzy min-cost capacitated network, Evolutionary algorithms
Abstract
Fuzzy optimization problems to which traditional methods - calculus and crisp algorithms - are not directly applicable have not been completely solved. We used evolutionary algorithms to produce good approximate solutions to fuzzy optimization problems including fully fuzzified linear programming, nonlinear fuzzy regression, neural net training, and fuzzy hierarchical analysis. We applied our evolutionary algorithm package to generating good approximate solutions to fuzzy optimization problems in operations research including the fuzzy shortest route problem and the fuzzy min-cost capacitated flow problem.
Cite this article as:J. Buckley, T. Feuring, and Y. Hayashi, “Solving Fuzzy Problems in Operations Research,” J. Adv. Comput. Intell. Intell. Inform., Vol.3 No.3, pp. 171-176, 1999.Data files:
