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Article . 2017
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Article . 2017 . Peer-reviewed
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Article . 2020
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Finding a solution for Multi-Objective Linear Fractional Programming problem based on goal programming and Data Envelopment Analysis

Finding a solution for multi-objective linear fractional programming problem based on goal programming and data envelopment analysis
Authors: Gholam Reza Jahanshahloo; Bahram Talebian; F. Hosseinzadeh Lotfi; Jafar Sadeghi 0001;

Finding a solution for Multi-Objective Linear Fractional Programming problem based on goal programming and Data Envelopment Analysis

Abstract

Summary: The multi-objective linear fractional programming is an interesting topic with many applications in different fields. Until now, various algorithms have been proposed in order to solve the multi-objective linear fractional programming (MOLFP) problem. An important point in most of them is the use of non-linear programming with a high computational complexity or the use of linear programming with preferences of the objective functions which are assigned by the decision maker. The current paper, through combining goal programming and data envelopment analysis (DEA), proposes an iterative method to solve MOLFP problems using only linear programming. Moreover, the proposed method provides an efficient solution which fairly optimizes each objective function when the decision maker has no information about the preferences of the objective functions. In fact, along with normalization of the objective functions, their relative preferences are fairly determined using the DEA. The implementation of the proposed method is demonstrated using numerical examples.

Related Organizations
Keywords

goal programming, Linear programming, fair satisfaction, data envelopment analysis, Fractional programming, Multi-objective and goal programming, multi-objective linear fractional programming

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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!
6
Top 10%
Average
Average
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