A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems

Hideki KATAGIRI; El Bekkaye MERMRI; Masatoshi SAKAWA; Kosuke KATO; Ichiro NISHIZAKI

doi:10.1093/ietisy/e88-d.8.1912

A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems

Hideki KATAGIRI, El Bekkaye MERMRI, Masatoshi SAKAWA, Kosuke KATO, Ichiro NISHIZAKI

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E88-D No.8 pp.1912-1919

Publication Date: 2005/08/01

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DOI: 10.1093/ietisy/e88-d.8.1912

Type of Manuscript: Special Section PAPER (Special Section on Recent Advances in Circuits and Systems--Part 2)

Category: Neural Networks and Fuzzy Systems

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Hideki KATAGIRI, El Bekkaye MERMRI, Masatoshi SAKAWA, Kosuke KATO, Ichiro NISHIZAKI, "A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems" in IEICE TRANSACTIONS on Information, vol. E88-D, no. 8, pp. 1912-1919, August 2005, doi: 10.1093/ietisy/e88-d.8.1912.
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.8.1912/_p

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@ARTICLE{e88-d_8_1912,
author={Hideki KATAGIRI, El Bekkaye MERMRI, Masatoshi SAKAWA, Kosuke KATO, Ichiro NISHIZAKI, },
journal={IEICE TRANSACTIONS on Information},
title={A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems},
year={2005},
volume={E88-D},
number={8},
pages={1912-1919},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e88-d.8.1912},
ISSN={},
month={August},}

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TY - JOUR
TI - A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems
T2 - IEICE TRANSACTIONS on Information
SP - 1912
EP - 1919
AU - Hideki KATAGIRI
AU - El Bekkaye MERMRI
AU - Masatoshi SAKAWA
AU - Kosuke KATO
AU - Ichiro NISHIZAKI
PY - 2005
DO - 10.1093/ietisy/e88-d.8.1912
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2005
AB - 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.
ER -