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Solving Fuzzy Linear Programming Problems Using Two-Level Programming Approach

English
Authors: H. A Khalifa;

Solving Fuzzy Linear Programming Problems Using Two-Level Programming Approach

Abstract

Linear programming (LP) has been widely applied to solving real world problems. The conventional LP model requires the parameters to be known constants. In the real world, however, the parameters are seldom known exactly and have to be estimated. This paper deals a linear programming (FLP) problem with fuzzy parameters. The problem is considered by incorporating fuzzy numbers in the cost coefficients, required coefficients, and technological coefficients. Through the use of the  -level sets of fuzzy numbers, the FLP problem is converted to the corresponding  -parametric LP problem ( -PLP) and hence to interval linear programming (ILP) problem. A pair of two-level mathematical programs is formulated to calculate the lower bound (Lb) and upper bound (Ub) of the objective values of ILP problem. The two-level mathematical programs are then transformed into one-level nonlinear programs. Solving the pair of nonlinear programs produces the interval of the objective values of the problem. An illustrative numerical example is provided in the sake of the paper to clarify the proposed approach.

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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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!
0
Average
Average
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