ID | 115814 |
Author |
Katagiri, Hideki
Kanagawa University
Kato, Kosuke
Hiroshima Institute of Technology
Uno, Takeshi
Tokushima University
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Keywords | discrete fuzzy random variable
linear programming
possibility measure
necessity measure
expectation model
Pareto optimal solution
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Content Type |
Journal Article
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Description | This paper considers linear programming problems (LPPs) where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables). New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments.
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Journal Title |
Symmetry
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ISSN | 20738994
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Publisher | MDPI
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Volume | 9
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Issue | 11
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Start Page | 254
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Published Date | 2017-10-30
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Rights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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language |
eng
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departments |
Science and Technology
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