A Kaleodoscopic View of Fuzzy Stochastic Optimization*
A Kaleodoscopic View of Fuzzy Stochastic Optimization*作者机构：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
出 版 物：《American Journal of Operations Research》 (美国运筹学期刊（英文）)
年 卷 期：2021年第11卷第6期
页 面：283-308页
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Optimization Randomness Fuzziness Fuzzy Random Variable
摘 要：The last three decades have witnessed development of optimization under fuzziness and randomness also called Fuzzy Stochastic Optimization. The main objective of 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.
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