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...
Slobodan Vujić, Goran Ćirović (1996)
The Yugoslav Journal of Operations Research
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Plamen P. Angelov (1994)
The Yugoslav Journal of Operations Research
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Jaroslav Ramík (1983)
Kybernetika
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José M. Cadenas, Fernando Jiménez (1996)
Mathware and Soft Computing
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In this paper, we propose a relationship of fuzzy duality. We use the Decomposition Theorem and some properties about Linear Programming with interval coefficients to define this relationship. Thus, a linear programming problem with fuzzy costs represented by membership functions L-R can be solved by means of two dual problems (linear programming problems with fuzzy constraints). Moreover, these results can be applied to multiobjective problems whose coefficients of the objective function...
Milanka Gardašević-Filipović, Dragan Z. Šaletić (2010)
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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Nirmal Kumar Mandal (2012)
The Yugoslav Journal of Operations Research
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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.
Branko Tadić, Danijela Tadić, Nenad Marjanović (2007)
The Yugoslav Journal of Operations Research
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