A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems

Hideki KATAGIRI
El Bekkaye MERMRI
Masatoshi SAKAWA
Kosuke KATO
Ichiro NISHIZAKI

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E88-D    No.8    pp.1912-1919
Publication Date: 2005/08/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.8.1912
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Recent Advances in Circuits and Systems--Part 2)
Category: Neural Networks and Fuzzy Systems
Keyword: 
fuzzy random variable,  minimum spanning tree problem,  possibility theory,  expectation optimization model,  polynomial-time algorithm,  

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Summary: 
This paper deals with minimum spanning tree problems where each edge weight is a fuzzy random variable. In order to consider the imprecise nature of the decision maker's judgment, a fuzzy goal for the objective function is introduced. A novel decision making model is constructed based on possibility theory and on a stochastic programming model. It is shown that the problem including both randomness and fuzziness is reduced to a deterministic equivalent problem. Finally, a polynomial-time algorithm is provided to solve the problem.

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