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Hitoshi Yano

Researcher at Kagawa University

Publications -  30
Citations -  1181

Hitoshi Yano is an academic researcher from Kagawa University. The author has contributed to research in topics: Fuzzy logic & Fuzzy number. The author has an hindex of 16, co-authored 30 publications receiving 1142 citations.

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An Interactive Fuzzy Satisficing Method for Multiobjective Linear-Programming Problems and Its Application

TL;DR: A new interactive fuzzy satisficing method is presented for solving multiobjective linear-programming problems by assuming that the decisionmaker (DM) has fuzzy goals for each of the objective functions through the interaction with the DM.
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Interactive decision making for multiobjective nonlinear programming problems with fuzzy parameters

TL;DR: Based on the proposed methods for multiobjective linear, linear fractional and nonlinear programming problems with fuzzy parameters, interactive computer programs are developed and an illustrative numerical example for nonlinear case is demonstrated.
Journal ArticleDOI

An interactive fuzzy satisficing method for multiobjective nonlinear programming problems with fuzzy parameters

TL;DR: An interactive fuzzy satisficing method for multiobjective nonlinear programming problems with fuzzy numbers that can be derived efficiently from among an M-α-Pareto optimal solution set is presented.
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An interactive fuzzy satisficing method using augmented minimax problems and its application to environmental systems

TL;DR: A new interactive fuzzy satisficing method for multiobjective nonlinear programming is presented which considers that the decision-maker (DM) has fuzzy goals for each of the objective functions through the interaction with the DM.
Journal ArticleDOI

An interactive fuzzy satisficing method for generalized multiobjective linear programming problems with fuzzy parameters

TL;DR: In a new interactive fuzzy satisficing method for multiobjective linear programming problems with fuzzy parameters, the satisficing solution of the decision maker is derived efficiently from among M-α-Pareto optimal solutions.