A Kaleodoscopic View of Fuzzy Stochastic Optimization*  

作　　者：Yves Tinda Mangongo Justin Dupar Busili Kampempe Monga Kalonda Luhandjula Yves Tinda Mangongo;Justin Dupar Busili Kampempe;Monga Kalonda Luhandjula(Département de Mathématiques et Informatique, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo;Département de Mathématique-Informatique, Université de Kisangani, Kisangani, Democratic Republic of the Congo)

机构地区：[1]Département de Mathématiques et Informatique, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo [2]Département de Mathématique-Informatique, Université de Kisangani, Kisangani, Democratic Republic of the Congo

出　　处：《American Journal of Operations Research》2021年第6期283-308,共26页美国运筹学期刊（英文）

摘　　要：The last three decades ha</span><span style="font-family:"">ve</span><span style="font-family:""> witnessed development of optimization under fuzziness and randomness also called Fuzzy Stochastic Optimization. The main objective </span><span style="font-family:"">of </span><span style="font-family:"">this new field is the need for basing many human decisions on information which is both fuzzily imprecise and probabilistically uncertain. Consistency indexes providing a union nexus between possibilities and probabilities of uncertain events exist in the literature. Nevertheless, there are no reliable transformations between them. This calls for new paradigms for coping with mathematical models involving both fuzziness and randomness. Fuzzy Stochastic Optimization (FSO) is an attempt to fulfill this need. In this paper, we present a panoramic view of Fuzzy Stochastic Optimization emphasizing the methodological aspects. The merits of existing methods are also briefly discussed along with some related theoretical aspects.The last three decades ha</span><span style="font-family:"">ve</span><span style="font-family:""> witnessed development of optimization under fuzziness and randomness also called Fuzzy Stochastic Optimization. The main objective </span><span style="font-family:"">of </span><span style="font-family:"">this new field is the need for basing many human decisions on information which is both fuzzily imprecise and probabilistically uncertain. Consistency indexes providing a union nexus between possibilities and probabilities of uncertain events exist in the literature. Nevertheless, there are no reliable transformations between them. This calls for new paradigms for coping with mathematical models involving both fuzziness and randomness. Fuzzy Stochastic Optimization (FSO) is an attempt to fulfill this need. In this paper, we present a panoramic view of Fuzzy Stochastic Optimization emphasizing the methodological aspects. The merits of existing methods are also briefly discussed along with some related theoretical aspects.

关 键 词：OPTIMIZATION RANDOMNESS FUZZINESS Fuzzy Random Variable 

分 类 号：O15[理学—数学]