Solving a multiobjective possibilistic problem through compromise programming

Real decision problems usually consider several objectives that have parameters which are often given by the decision maker in an imprecise way. It is possible to handle these kinds of problems through multiple criteria models in terms of possibility theory. Here we propose a method for solving thes...

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Bibliographic Details
Published in:European journal of operational research 2005-08, Vol.164 (3), p.748-759
Main Authors: Arenas Parra, M., Bilbao Terol, A., Pérez Gladish, B., Rodrı́guez Urı́a, M.V.
Format: Article
Language:English
Subjects:
Applied sciences
Compromise programming
Decision making
Decision theory. Utility theory
Exact sciences and technology
Expected interval
Fuzzy number
Linear programming
Mathematical models
Multiobjective programming
Multiple criteria decision making
Operational research and scientific management
Operational research. Management science
Operations research
Possibility distribution
Studies
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Summary:Real decision problems usually consider several objectives that have parameters which are often given by the decision maker in an imprecise way. It is possible to handle these kinds of problems through multiple criteria models in terms of possibility theory. Here we propose a method for solving these kinds of models through a fuzzy compromise programming approach. To formulate a fuzzy compromise programming problem from a possibilistic multiobjective linear programming problem the fuzzy ideal solution concept is introduced. This concept is based on soft preference and indifference relationships and on canonical representation of fuzzy numbers by means of their α-cuts. The accuracy between the ideal solution and the objective values is evaluated handling the fuzzy parameters through their expected intervals and a definition of discrepancy between intervals is introduced in our analysis.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2003.11.028