Dependent-chance programming with fuzzy decisions

Author

Liu, Baoding

Author_Institution

Dept. of Math. Sci., Tsinghua Univ., Beijing, China

Volume

7

Issue

3

fYear

1999

fDate

6/1/1999 12:00:00 AM

Firstpage

354

Lastpage

360

Abstract

Dependent-chance programming (DCP) is a new type of stochastic programming and has been extended to the area of fuzzy programming. This paper provides a spectrum of DCP and dependent-chance multiobjective programming (DCMOP) as well as dependent-chance goal programming (DCGP) models with fuzzy rather than crisp decisions. The terms of uncertain environment, event, chance function, and induced constraints are discussed in the case of fuzzy decisions. A technique of fuzzy simulation is also designed for computing chance functions. Finally, we present a fuzzy simulation-based genetic algorithm for solving these models and illustrate its effectiveness by some numerical examples

Keywords

fuzzy set theory; genetic algorithms; stochastic programming; DCGP; DCMOP; DCP; chance function; dependent-chance goal programming; dependent-chance multiobjective programming; fuzzy decisions; fuzzy programming; fuzzy simulation-based genetic algorithm; induced constraints; stochastic programming; uncertain environment; Algorithm design and analysis; Computational modeling; Fuzzy sets; Fuzzy systems; Genetic algorithms; Linear programming; Mathematical model; Mathematical programming; Stochastic processes; Stochastic systems;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.771090

Filename

771090