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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/11446

Title: Solving a multiobjective possibilistic problem through compromise programming
Author(s): Arenas Parra, María del Mar
Bilbao Terol, Amelia María
Pérez Gladish, Blanca María
Rodríguez Uría, María Victoria
Keywords: Multiple Criteria Decision Making; Multiobjective Programming; Compromise Programming; Fuzzy Number; Possibility Distribution; Expected Interval
Issue date: 2005
Publisher: Elsevier
Publisher version: http://dx.doi.org/10.1016/j.ejor.2003.11.028
Citation: European Journal of Operational Research, 164(3), P. 748-759 (2005); doi:10.1016/j.ejor.2003.11.028
Format extent: 748-759
Abstract: 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.
URI: http://www.sciencedirect.com/science/article/pii/S0377221704000499
ISSN: 0377-2217
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