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zbMATH Open
Article . 2000
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Solving a possibilistic linear program through compromise programming

Authors: Jiménez López, Mariano; Rodríguez Uría, Ma. Victoria; Arenas Parra, Mar; Bilbao Terol, Amelia;

Solving a possibilistic linear program through compromise programming

Abstract

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 $\alpha$-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 of the objective function. Then DM faces a bi-objective problem that we will solve through a Compromise Programming approach, whose solution lets the Decision-Maker express his own preferences about feasibility versus optimality. Our proposed method will be illustrated by a numerical example

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Spain
Keywords

Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming, compromise programming, possibility distributions, linear programming, \(\alpha\)-degree feasible solutions, fuzzy number ranking, Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming, fuzzy parameters, Fuzzy numbers ranking., expected interval, expected value, preference relationship of fuzzy numbers, Linear programming, Programació (Matemàtica), Possibility distribution, Fuzzy and other nonstochastic uncertainty mathematical programming, Fuzzy numbers ranking, Multi-objective and goal programming, Compromise programming, Expected interval, Expected value, Sistemes experts (Informàtica)

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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