A random fuzzy programming models based on possibilistic programming

Hideki Katagiri; Takashi Hasuike; Hiroaki Ishii

doi:10.1109/ICSMC.2008.4811548

A random fuzzy programming models based on possibilistic programming

Hideki Katagiri, Takashi Hasuike, Hiroaki Ishii

Research output: Contribution to journal › Conference article › peer-review

13 Citations (Scopus)

Abstract

This paper considers linear programming problems where each coefficient of the objective function is expressed by a random fuzzy variable. New decision making models are proposed based on stochastic and possibilistic programming in order to maximize both of possibility and probability with respect to the objective function value. It is shown that each of the proposed models is transformed into a deterministic equivalent one. Solution algorithms using convex programming techniques and/or the bisection method are provided for obtaining an optimal solution of each model.

Original language	English
Article number	4811548
Pages (from-to)	1788-1793
Number of pages	6
Journal	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
DOIs	https://doi.org/10.1109/ICSMC.2008.4811548
Publication status	Published - 2008 Dec 1
Externally published	Yes
Event	2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore Duration: 2008 Oct 12 → 2008 Oct 15

Keywords

Linear program
Random fuzzy programming models
Random fuzzy variable

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

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abstract = "This paper considers linear programming problems where each coefficient of the objective function is expressed by a random fuzzy variable. New decision making models are proposed based on stochastic and possibilistic programming in order to maximize both of possibility and probability with respect to the objective function value. It is shown that each of the proposed models is transformed into a deterministic equivalent one. Solution algorithms using convex programming techniques and/or the bisection method are provided for obtaining an optimal solution of each model.",

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AB - This paper considers linear programming problems where each coefficient of the objective function is expressed by a random fuzzy variable. New decision making models are proposed based on stochastic and possibilistic programming in order to maximize both of possibility and probability with respect to the objective function value. It is shown that each of the proposed models is transformed into a deterministic equivalent one. Solution algorithms using convex programming techniques and/or the bisection method are provided for obtaining an optimal solution of each model.

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