Abstract
This paper considers an expectation optimization model using a possibility measure to the objective function in the fuzzy random programming problem, based on possibilistic programming and stochastic programming. The main fuzzy random programming problem is not a well-defined problem due to including random variables and fuzzy numbers. Therefore, in order to solve it analytically, a criterion for goal of objective function is set and the chance constraint are introduced. Then, considering decision maker's subjectivity and flexibility of the original plan, a fuzzy goal for each objective function is introduced. Furthermore, this paper considers that the occurrence probability of each scenario has ambiguity, and is represented as an interval value. Considering this interval of probability, a robust expectation optimization problem is proposed. Main problem is transformed into the deterministic equivalent linear programming problem, and so the analytical solution method extending previous solution approaches is constructed.
| Original language | English |
|---|---|
| Title of host publication | Advances in Intelligent and Soft Computing |
| Publisher | Springer Verlag |
| Pages | 285-294 |
| Number of pages | 10 |
| Volume | 58 |
| ISBN (Print) | 9783540896180 |
| Publication status | Published - 2009 |
| Externally published | Yes |
Publication series
| Name | Advances in Intelligent and Soft Computing |
|---|---|
| Volume | 58 |
| ISSN (Print) | 18675662 |
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ASJC Scopus subject areas
- Computer Science(all)
Cite this
Robust expectation optimization model using the possibility measure for the fuzzy random programming problem. / Hasuike, Takashi; Ishii, Hiroaki.
Advances in Intelligent and Soft Computing. Vol. 58 Springer Verlag, 2009. p. 285-294 (Advances in Intelligent and Soft Computing; Vol. 58).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Robust expectation optimization model using the possibility measure for the fuzzy random programming problem
AU - Hasuike, Takashi
AU - Ishii, Hiroaki
PY - 2009
Y1 - 2009
N2 - This paper considers an expectation optimization model using a possibility measure to the objective function in the fuzzy random programming problem, based on possibilistic programming and stochastic programming. The main fuzzy random programming problem is not a well-defined problem due to including random variables and fuzzy numbers. Therefore, in order to solve it analytically, a criterion for goal of objective function is set and the chance constraint are introduced. Then, considering decision maker's subjectivity and flexibility of the original plan, a fuzzy goal for each objective function is introduced. Furthermore, this paper considers that the occurrence probability of each scenario has ambiguity, and is represented as an interval value. Considering this interval of probability, a robust expectation optimization problem is proposed. Main problem is transformed into the deterministic equivalent linear programming problem, and so the analytical solution method extending previous solution approaches is constructed.
AB - This paper considers an expectation optimization model using a possibility measure to the objective function in the fuzzy random programming problem, based on possibilistic programming and stochastic programming. The main fuzzy random programming problem is not a well-defined problem due to including random variables and fuzzy numbers. Therefore, in order to solve it analytically, a criterion for goal of objective function is set and the chance constraint are introduced. Then, considering decision maker's subjectivity and flexibility of the original plan, a fuzzy goal for each objective function is introduced. Furthermore, this paper considers that the occurrence probability of each scenario has ambiguity, and is represented as an interval value. Considering this interval of probability, a robust expectation optimization problem is proposed. Main problem is transformed into the deterministic equivalent linear programming problem, and so the analytical solution method extending previous solution approaches is constructed.
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UR - http://www.scopus.com/inward/citedby.url?scp=84856543265&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84856543265
SN - 9783540896180
VL - 58
T3 - Advances in Intelligent and Soft Computing
SP - 285
EP - 294
BT - Advances in Intelligent and Soft Computing
PB - Springer Verlag
ER -