A Fuzzy Goal Programming Approach for Solving ‎Multi-Objective Minimum Cost Flow Problems ‎with Possibilistic Coefficients

Authors

  • Robert S. Keyser Southern Polytechnic College of Engineering and Engineering Technology, Kennesaw State University, Marietta, Georgia ‎‎30060, USA‎
  • Hamiden Abd El-Wahed Khalifa Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt.‎

Keywords:

Minimum cost flow, Multi-objective optimization, Possibilistic variables, Fuzzy goal programming approach, α-possibly optimal solution, Goal programming, Compromise solution, Parametric analysis

Abstract

This paper studies a multi-objective Minimum Cost Flow (MCF) with possibilistic objective function coefficients. A necessary and sufficient condition for investigating the α-possibly optimal solution is established. A fuzzy Goal Programming (GP) approach is applied to obtain the α-parametric optimal compromise solution. The parametric study under the concept of α-possibly optimal solution is analyzed without differentiability.  Finally, a numerical example is given for the paper to clarify the methodology.

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Published

2024-03-10

How to Cite

A Fuzzy Goal Programming Approach for Solving ‎Multi-Objective Minimum Cost Flow Problems ‎with Possibilistic Coefficients. (2024). Uncertainty Discourse and Applications, 1(1), 29-40. https://uda.reapress.com/journal/article/view/20

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