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Katagiri, H., Mermeri, L.B., Sakawa, M., Kato, K. and Nishihashi, L. (2005) A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems. IEICE Transaction on Information Systems, E88-D, 1912-1919.
https://doi.org/10.1093/ietisy/e88-d.8.1912

has been cited by the following article:

• TITLE: A Kaleodoscopic View of Fuzzy Stochastic Optimization*

  AUTHORS: Yves Tinda Mangongo, Justin Dupar Busili Kampempe, Monga Kalonda Luhandjula

  KEYWORDS: Optimization, Randomness, Fuzziness, Fuzzy Random Variable

  JOURNAL NAME: American Journal of Operations Research, Vol.11 No.6, November 2, 2021

  ABSTRACT: 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.