Abstract
In order to solve the optimization problem of selecting the decision with maximal chance to meet the Sugeno event in Sugeno environment, dependent-chance programming on Sugeno measure space is proposed, which can be considered as a generalized extension of the stochastic dependent-chance programming. Firstly, the theoretical framework of dependent-chance programming on Sugeno measure space is established. Secondly, a Sugeno simulation-based hybrid approach, which consists of back propagation neural network and genetic algorithm, is presented to construct an approximate solution of the complex dependent-chance programming models on Sugeno measure space. Finally, some numerical examples are given to illustrate the effectiveness of the approach.
Highlights
There are a lot of uncertainties in decision sciences, engineering, information sciences, system sciences, etc
"Dependent-chance Programming on Sugeno Measure Space" section firstly proposes the concepts of Sugeno environment, event and chance function, and gives the principle of uncertainty which is the theoretical basis of the dependent-chance programming (DCP) on Sugeno measure space
To provide general solutions to the programming, a Sugeno simulation-based hybrid approach integrated by back propagation (BP) neural network and genetic algorithm (GA) was given
Summary
There are a lot of uncertainties in decision sciences, engineering, information sciences, system sciences, etc. "Dependent-chance Programming on Sugeno Measure Space" section firstly proposes the concepts of Sugeno environment, event and chance function, and gives the principle of uncertainty which is the theoretical basis of the DCP on Sugeno measure space. N D1⁄2rðx; x2; x3; x4; x5; x6Þ 1⁄4 fx; x2; x5g: Definition 15 Let x be a decision vector, ξ be a gλ vector, and E be an event characterized by hk(x, ξ) ≤ 0, k = 1, 2, ⋯, q in the Sugeno environment gj(x, ξ) ≤ 0, j = 1, 2, ⋯, p. We obtain the following principle of uncertainty in the Sugeno environment which is theoretical basis of DCP on Sugeno measure space. In order to maximize the chance function of an event subject to a Sugeno environment, we give the following dependent-chance single-objective programming on Sugeno measure space:.
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