H
Hideki Katagiri
Researcher at Kanagawa University
Publications - 220
Citations - 2076
Hideki Katagiri is an academic researcher from Kanagawa University. The author has contributed to research in topics: Fuzzy logic & Stochastic programming. The author has an hindex of 25, co-authored 217 publications receiving 2006 citations. Previous affiliations of Hideki Katagiri include Hiroshima University & Osaka University.
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Portfolio selection problems with random fuzzy variable returns
TL;DR: This paper considers several portfolio selection problems including probabilistic future returns with ambiguous expected returns assumed as random fuzzy variables, and their efficient solution methods to find a global optimal solution of each problem is constructed.
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Interactive multiobjective fuzzy random linear programming : Maximization of possibility and probability
TL;DR: A new decision making model is proposed to maximize both possibility and probability, which is based on possibilistic programming and stochastic programming, and an interactive algorithm is constructed to obtain a satisficing solution satisfying at least weak Pareto optimality.
Portfolio selection problems withrandomfuzzy variable returns
Takashi Hasuike,Hideki Katagiri +1 more
Journal ArticleDOI
An interactive fuzzy satisficing method for multiobjective linear programming problems with random variable coefficients through a probability maximization model
TL;DR: An interactive fuzzy satisficing method to derive a satisficing solution for the decision maker by updating the reference membership levels is presented and an illustrative numerical example is provided to demonstrate the feasibility of the proposed method.
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Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm
TL;DR: A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system and the results are compared against the results of a discrete-time approximation technique to show the efficiency of the proposed GA approach.