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Fuzzy number linear programming: A probabilistic approach (3)

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Abstract

In the real world there are many linear programming problems where all decision parameters are fuzzy numbers. Several approaches exist which use different ranking functions for solving these problems. Unfortunately when there exist alternative optimal solutions, usually with different fuzzy value of the objective function for these solutions, these methods can not specify a clear approach for choosing a solution. In this paper we propose a method to remove the above shortcoming in solving fuzzy number linear programming problems using the concept of expectation and variance as ranking functions.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Sciences, Shahid Bahonar University of Kerman, Kerman, Iran

    H. R. Maleki & M. Mashinchi

Authors
  1. H. R. Maleki
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  2. M. Mashinchi
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Correspondence to H. R. Maleki.

Additional information

H. R. Maleki received his B.Sc from Shiraz University in Iran, M.Sc and Ph.D (in 1999) both from Shahid Bahonar University of Kerman in Iran. He is Assistant Professor of mathematics in Shahid Bahonar University of Kerman. His research interest is fuzzy operation research and graph theory.

M. Mashinchi received his B.Sc. and M.Sc in Iran from Ferdowsi University and Shiraz University, respectively and his Ph.D in Japan from Waseda University (in 1978). He is Professor of mathematics in Shahid Bahonar University of Kerman. His research interest is in fuzzy mathematics, especially on decision making and algebric systems.

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Maleki, H.R., Mashinchi, M. Fuzzy number linear programming: A probabilistic approach (3). JAMC 15, 333–341 (2004). https://doi.org/10.1007/BF02935766

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  • DOI: https://doi.org/10.1007/BF02935766

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