Path: Top -> Journal -> Jurnal Internasional -> Fuzzy Information and Engineering -> 2021 -> Volume 13, Issue 3

A New Method to Solve Multi-Objective Linear Fractional Problems

Journal from gdlhub / 2022-02-16 15:27:06
By : Mojtaba Borza & Azmin Sham Rambely, Fuzzy Information and Engineering
Created : 2022-02-16, with 0 files

Keyword : Efficient solution, membership function, max–min technique, linear programming, fractional programming
Url : http://www.tandfonline.com/doi/full/10.1080/16168658.2021.1938868
Document Source : web

Background: In the literature, there exists several approaches to address the multi-objective linear fractional programming problem (MOLFPP). However, there is a drawback to these methods.

Aim: This paper presents an efficient method treating the MOLFPP.

Methodology: To construct our approach,the membership functions of the objectives, suitable non-linear variable transformations, and max-min technique are used.

Results: In our proposed method, the MOLFPP is finally changed into a linear programming problem (LPP). It is proven that the optimal solution of the LPP is an efficient solution for the MOLFPP.

Conclusion: Numerical examples are solved, and the results demonstrate that our method with less computational expenses and cost reach the efficient solutions.

Description Alternative :

Background: In the literature, there exists several approaches to address the multi-objective linear fractional programming problem (MOLFPP). However, there is a drawback to these methods.

Aim: This paper presents an efficient method treating the MOLFPP.

Methodology: To construct our approach,the membership functions of the objectives, suitable non-linear variable transformations, and max-min technique are used.

Results: In our proposed method, the MOLFPP is finally changed into a linear programming problem (LPP). It is proven that the optimal solution of the LPP is an efficient solution for the MOLFPP.

Conclusion: Numerical examples are solved, and the results demonstrate that our method with less computational expenses and cost reach the efficient solutions.

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