Penalty Method for Fuzzy Linear Programming With Trapezoidal Numbers
Bogdana Stanojević, Milan Stanojević (2009)
The Yugoslav Journal of Operations Research
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Bogdana Stanojević, Milan Stanojević (2009)
The Yugoslav Journal of Operations Research
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Mariano Jiménez López, María Victoria Rodríguez Uría, María del Mar Arenas Parra, Amelia Bilbao Terol (2000)
Mathware and Soft Computing
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In this paper we propose a method to solve a linear programming problem involving fuzzy parameters whose possibility distributions are given by fuzzy numbers. To address the above problem we have used a preference relationship of fuzzy numbers that leads us to a solving method that produces the so-called α-degree feasible solutions. It must be pointed out that the final solution of the problem depends critically on this degree of feasibility, which is in conflict with the optimal value...
José M. Cadenas, Fernando Jiménez (1994)
Mathware and Soft Computing
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We present an interactive decision support system which aids in solving a general multiobjective fuzzy problem, that is, a multiobjective programming problem with fuzzy goals subject to a fuzzy constraint set. The interactive decision support system is proposed. After eliciting the fuzzy goals of the decision maker for each objective function and the fuzzy elements for each constraint, the satisfactory solutions for the decision maker were derived by interactively updating the reference...
D. Pandey, S. Kumar (2012)
The Yugoslav Journal of Operations Research
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Jaroslav Ramík (1983)
Kybernetika
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Slobodan Vujić, Goran Ćirović (1996)
The Yugoslav Journal of Operations Research
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B. Stanojević, I.M. Stancu-Minasian (2012)
The Yugoslav Journal of Operations Research
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Plamen P. Angelov (1994)
The Yugoslav Journal of Operations Research
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Nasseri, S.H. (2008)
The Journal of Nonlinear Sciences and its Applications
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Hassan Hassanpour, Elham Hosseinzadeh, Mahsa Moodi (2023)
Applications of Mathematics
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The aim of this paper is solving an intuitionistic fuzzy multi-objective linear programming problem containing intuitionistic fuzzy parameters, intuitionistic fuzzy maximization/minimization, and intuitionistic fuzzy constraints. To do this, a linear ranking function is used to convert the intuitionistic fuzzy parameters to crisp ones first. Then, linear membership and non-membership functions are used to manipulate intuitionistic fuzzy maximization/minimization and intuitionistic fuzzy...
Rita Almeida Ribeiro, Fernando Moura Pires (1999)
Kybernetika
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This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solving fuzzy optimization problems.