Chance-constrained programming on sugeno measure space

被引:8
|
作者
Zhang, Hong [1 ]
Ha, Minghu [2 ]
Xing, Hongjie [2 ]
机构
[1] Hebei Univ Engn, Coll Sci, Handan 056038, Hebei, Peoples R China
[2] Hebei Univ, Coll Math & Comp Sci, Baoding 071002, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Sugeno measure; g(lambda) random variable; alpha-optimistic value; alpha-pessimistic value; Sugeno chance-constrained programming; EXPECTED VALUE MODELS; HYBRID INTELLIGENT ALGORITHM; FUZZY MEASURES; OPTIMIZATION; NETWORKS;
D O I
10.1016/j.eswa.2011.03.029
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Uncertain programming is a theoretical tool to handle optimization problems under uncertain environment, it is mainly established in probability, possibility, or credibility measure spaces. Sugeno measure space is an interesting and important extension of probability measure space. This motivates us to discuss the uncertain programming based on Sugeno measure space. We have constructed the first type of uncertain programming on Sugeno measure space, i.e. the expected value models of uncertain programming on Sugeno measure space. In this paper, the second type of uncertain programming on Sugeno measure space, i.e. chance-constrained programming on Sugeno measure space, is investigated. Firstly, the definition and the characteristic of alpha-optimistic value and alpha-pessimistic value as a ranking measure are provided. Secondly, Sugeno chance-constrained programming (SCCP) is introduced. Lastly, in order to construct an approximate solution to the complex SCCP, the ideas of a Sugeno random number generation and a Sugeno simulation are presented along with a hybrid approach. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:11527 / 11533
页数:7
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