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

Access to Document

10.1109/ICSMC.2008.4811548

Fingerprint Dive into the research topics of 'A random fuzzy programming models based on possibilistic programming'. Together they form a unique fingerprint.

Cite this

@article{27c1afd97c664eb7904262486a254540,

title = "A random fuzzy programming models based on possibilistic programming",

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.",

keywords = "Linear program, Random fuzzy programming models, Random fuzzy variable",

author = "Hideki Katagiri and Takashi Hasuike and Hiroaki Ishii",

year = "2008",

month = dec,

day = "1",

doi = "10.1109/ICSMC.2008.4811548",

language = "English",

pages = "1788--1793",

journal = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

issn = "1062-922X",

note = "2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 ; Conference date: 12-10-2008 Through 15-10-2008",

}

TY - JOUR

T1 - A random fuzzy programming models based on possibilistic programming

AU - Katagiri, Hideki

AU - Hasuike, Takashi

AU - Ishii, Hiroaki

PY - 2008/12/1

Y1 - 2008/12/1

N2 - 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.

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.

KW - Linear program

KW - Random fuzzy programming models

KW - Random fuzzy variable

UR - http://www.scopus.com/inward/record.url?scp=67650262610&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67650262610&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2008.4811548

DO - 10.1109/ICSMC.2008.4811548

M3 - Conference article

AN - SCOPUS:67650262610

SP - 1788

EP - 1793

JO - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

JF - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SN - 1062-922X

M1 - 4811548

T2 - 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008

Y2 - 12 October 2008 through 15 October 2008

ER -

A random fuzzy programming models based on possibilistic programming

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this