A Comparative Study of Meta-heuristic Algorithms in Supply Chain Networks

Today, with the development of Information Technology (IT) and economic globalization, the suppliers’ selection has been emphasized in supply chain systems. Accordingly, artificial intelligence-based methods have attracted much attention. Hence, in this research, the selection of appropriate supplie...

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Bibliographic Details
Published inJournal of industrial engineering international Vol. 17; no. 1; p. 52
Main Authors Salahi, Fariba, Daneshvar, Amir, Homayounfar, Mahdi, Shokouhifar, Mohammad
Format Journal Article
LanguageEnglish
Published Tehran Islamic Azad University, South Tehran Branch 01.03.2021
Subjects
Algorithms
Artificial intelligence
Comparative studies
Genetic algorithms
Globalization
Heuristic
Heuristic methods
Information technology
Optimization
Shortages
Simulated annealing
Suppliers
Supply chains
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Summary:Today, with the development of Information Technology (IT) and economic globalization, the suppliers’ selection has been emphasized in supply chain systems. Accordingly, artificial intelligence-based methods have attracted much attention. Hence, in this research, the selection of appropriate suppliers with respect to the multi-resource supply policy, and the implementation of lateral transshipment have been studied, and meta-heuristic algorithms have been employed to solve the problem. In the proposed method, the supply chain network is improved by minimizing the inventory shortages through utilizing lateral transshipment between different factories. In order to efficiently solve the problem, a hybrid meta-heuristic algorithm based on population-based genetic algorithm (GA) and single-solution simulated annealing (SA), named GASA, is propose, in order to simultaneously gain with the advantages of both algorithms, i.e., global search ability of GA and local search ability of SA. In order to compare the results of the proposed GASA, it is compared with GA and SA, to find the best solution. Given the parameters optimization and conducted analyses and comparisons of primary and hybrid algorithms performance, the hybrid GASA algorithm has been identified as the most efficient algorithm to solve the problem,compared to the other algorithms, emphasizing cost reduction and shortage volume.
ISSN:1735-5702
2251-712X
DOI:10.30495/jiei.2021.1919032.1076