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 |
|---|---|
| Title of host publication | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Pages | 1788-1793 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore Duration: 2008 Oct 12 → 2008 Oct 15 |
Other
| Other | 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 08/10/12 → 08/10/15 |
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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
Cite this
A random fuzzy programming models based on possibilistic programming. / Katagiri, Hideki; Hasuike, Takashi; Ishii, Hiroaki.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2008. p. 1788-1793 4811548.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A random fuzzy programming models based on possibilistic programming
AU - Katagiri, Hideki
AU - Hasuike, Takashi
AU - Ishii, Hiroaki
PY - 2008
Y1 - 2008
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 contribution
AN - SCOPUS:67650262610
SP - 1788
EP - 1793
BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ER -