Title :
Dependent-chance programming with fuzzy decisions
Author_Institution :
Dept. of Math. Sci., Tsinghua Univ., Beijing, China
fDate :
6/1/1999 12:00:00 AM
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;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
