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Random fuzzy dependent-chance programming and its hybrid intelligent algorithm. (English) Zbl 1175.90439
Summary: A fuzzy random variable is a measurable function from a probability space to a collection of fuzzy sets, while a random fuzzy variable is a function from a collection of random variables to \([0,1]\). This paper provides a spectrum of random fuzzy dependent-chance programming in which the underlying philosophy is based on selecting the decision with maximal chance to meet the event. In order to speed up the solution process, we train a neural network to approximate chance functions based on the training data generated by the random fuzzy simulation. Finally, we integrate random fuzzy simulation, neural network and genetic algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving random fuzzy dependent-chance programming models, and illustrate its effectiveness by some numerical examples.

MSC:
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
90C15 Stochastic programming
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