A Comparative Study of Meta-heuristic Algorithms in Supply Chain Networks
محورهای موضوعی : Artificial Intelligence
Fariba Salahi
1
(
Department of Industrial Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran
)
Amir Daneshvar
2
(
Department of Information Technology Management, Faculty of Management ,Electronic Branch, Islamic Azad University, Tehran, Iran.
)
Mahdi Homayounfar
3
(
Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran
)
Mohammad Shokouhifar
4
(
Department of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran
)
کلید واژه: Genetic Algorithm, supply chain management, Simulated Annealing, Multi-resource supplier selection, Lateral transshipment,
چکیده مقاله :
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 toefficiently 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.
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