Template-Type:ReDIF-Article 1.0

Author-Name:Kenan Karagül
Title:Comparative Analysis of Optimization Methods for Grey Fuzzy Transportation Problems in Logistics
Abstract:This study examines the Grey Fuzzy Transportation Problem, which represents decision-making processes under uncertainty
    in the transportation problem, a significant issue in the logistics sector and academic studies. The study provides
    comprehensive analysis and recommendations that contribute to the effective solution of the Grey Fuzzy Transportation
    Problem and better management of uncertain transportation problems. The research compares four different optimization
    methods, Closed Path Method, Interval Optimization, Robust Optimization, and Interval Optimization with Penalty
    Function, for the Grey Fuzzy Transportation Problem (GFTP). The analyses were conducted on a total of 40 test problems
    across four different problem sizes: small, medium, large, and extra-large. The results showed that the Interval
    Optimization and Robust Optimization methods demonstrated the best performance in terms of solution quality and
    computation time. Specifically, detailed analyses of the Interval Optimization with Penalty Function method confirmed
    that this method provides an effective and consistent solution approach for the GFTP.
Classification-JEL:C61, C63, C65
Keywords:Grey Fuzzy Linear Programming, JuMP, Julia, SCIP, Transportation Problem
Journal:Alphanumeric Journal
Pages:169-194
Volume:12
Issue:3
Year:2024
Month:December
DOI:https://doi.org/10.17093/alphanumeric.1503643
File-URL:https://www.alphanumericjournal.com/media/Issue/volume-12-issue-3-2024/comparative-analysis-of-optimization-methods-for-grey-fuzzy_T6ibrKg.pdf
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File-URL:https://alphanumericjournal.com/article/comparative-analysis-of-optimization-methods-for-grey-fuzzy-transportation-problems-in-logistics
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Handle: RePEc:anm:alpnmr:v:12:y:2024:i:3:p:169-194