Comparative Analysis of Optimization Methods for Grey Fuzzy Transportation Problems in Logistics

Kenan Karagül

Alphanumeric Journal, 2024, vol. 12, issue 3, 169-194

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.

Keywords: Grey Fuzzy Linear Programming; JuMP; Julia; SCIP; Transportation Problem (search for similar items in EconPapers)
JEL-codes: C61 C63 C65 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:12:y:2024:i:3:p:169-194

DOI: 10.17093/alphanumeric.1503643

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