A Comparative Study of Meta-heuristic Algorithms in Supply Chain Networks
محورهای موضوعی : Artificial Intelligence
Fariba Salahi
1
,
Amir Daneshvar
2
,
Mahdi Homayounfar
3
,
Mohammad Shokouhifar
4
1 - Department of Industrial Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran
2 - Department of Information Technology Management, Faculty of Management ,Electronic Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran
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.
[1] Akram, K., Kamal, K., & Zeb, A. (2016). “Fast simulated annealing
hybridized with quenching for solving job shop scheduling problem”,
Applied Soft Computing, 49, 510–523.
[2] Asgharizadeh, E., Behruz, M.S., & Fayaz Shahandoshti, F. (2015). “A
Mathematical Model for Suppliers' Selection Using a Multiple
Attribute Decision-making (MADM) Method”, Modiriat-e-Farda J,
44, 77–90.
[3] Atabaki, M. S., Khamseh, A. A., & Mohammadi, M. (2019). A
priority-based firefly algorithm for network design of a closed-loop
supply chain with price-sensitive demand. Computers & Industrial
Engineering, 135, 814-837.
[4] Ataee, N. (2015). “A Supply Portfolio Selection under Disruption
Risk Using Meta-heuristic Algorithms”, M.Sc. Thesis, Faculty of
Economics, Management and Administrative Sciences, Semnan
University, Semnan, Iran.
[5] Buhayenko, V., Ho, S. C., & Thorstenson, A. (2018). A variable
neighborhood search heuristic for supply chain coordination using
dynamic price discounts. EURO Journal on Transportation and
Logistics, 7(4), 363-385.
[6] Baliarsingh, S. K., Muhammad, K., & Bakshi, S. (2021). SARA: A
memetic algorithm for high-dimensional biomedical data. Applied
Soft Computing, 101, 107009.
[7] Chan, F.T.S., Kumar, N., Tiwari, M.K., Lau, H.C.W., & Choy, K.L.
(2008). “Global supplier selection: a fuzzy-AHP approach”, Int. J.
Production Research, 46, 3825–3857.
[8] Dzalbs, I., & Kalganova, T. (2020). Accelerating supply chains with
Ant Colony Optimization across a range of hardware
solutions. Computers & Industrial Engineering, 147, 106610.
[9] Fanian, F., Bardsiri, V. K., & Shokouhifar, M. (2018). A new task
scheduling algorithm using firefly and simulated annealing algorithms
in cloud computing. International Journal of Advanced Computer
Science and Applications, 9(2).
[10] Fathi, M., Khakifirooz, M., Diabat, A., & Chen, H. (2021). An
Integrated Queuing-Stochastic Optimization Hybrid Genetic
Algorithm for a Location-Inventory Supply Chain
Network. International Journal of Production Economics, 108139.
[11] Fathollahi-Fard, A. M., Govindan, K., Hajiaghaei-Keshteli, M., &
Ahmadi, A. (2019). A green home health care supply chain: New
modified simulated annealing algorithms. Journal of Cleaner
Production, 240, 118200.
[12] Firouz, M., Keskin, B.B., & Melouk, S.H. (2017). “An integrated
supplier selection and inventory problem with multi-sourcing and
lateral transshipments”, Omega, 70, 77-93.
[13] Jiang, J., Wu, D., Chen, Y., & Li, K. (2019). Complex network
oriented artificial bee colony algorithm for global bi-objective
optimization in three-echelon supply chain. Applied Soft
Computing, 76, 193-204.
[14] Kuhpfahl, J., & Bierwirth, C. (2016). “A study on local search
neighborhoods for the job shop scheduling problem with total
weighted tardiness objective”, Computers &. Operation. Research,
66, 44–57.
[15] Liu, P., & Zhang, X., (2011). “Research on the supplier selection of a
supply chain based on entropy weight and improved ELECTRE-III
method”, Int. J. Prod. Res, 49, 637–646.
[16] Luan, J., Yao, Z., Zhao, F., & Song, X. (2019). “A novel method to
solve supplier selection problem: Hybrid algorithm of genetic
algorithm and ant colony optimization. Math”, Comput. Simul, 156,
294–309.
[17] Mohammed, A. M., & Duffuaa, S. O. (2020). A tabu search based
algorithm for the optimal design of multi-objective multi-product
supply chain networks. Expert Systems with Applications, 140,
112808.
[18] Naderi, R., Nikabadi, M. S., Tabriz, A. A., & Pishvaee, M. S. (2021).
Supply chain sustainability improvement using exergy
analysis. Computers & Industrial Engineering, 154, 107142.
[19] Ramanathan, R. (2007). “Supplier selection problem: integrating DEA
with the approaches of total cost of ownership and AHP”, Int. J.
Supply Chain Manage, 12, 258–261.
[20] Ravindran, A.R., Bilsel, R.U., Wadhwa, V., & Yang, T. (2010). “Risk
adjusted multi criteria supplier selection models with applications”,
Int. J. Prod. Res, 48, 405–424.
[21] Rao, T.S. (2017). “A Comparative Evaluation of GA and SA TSP in
a Supply Chain Network. Mater”, Today Proc., 5th International
Conference of Materials Processing and Characterization (ICMPC
2016), 4, 2263–2268.
[22] Rostami, A., Paydar, M. M., & Asadi-Gangraj, E. (2020). A hybrid
genetic algorithm for integrating virtual cellular manufacturing with
supply chain management considering new product
development. Computers & Industrial Engineering, 145, 106565.
[23] Sabet, S., Shokouhifar, M., & Farokhi, F. (2016). A comparison
between swarm intelligence algorithms for routing
problems. Electrical & Computer Engineering: An International
Journal (ECIJ), 5(1), 17-33.
[24] Samouei, P., & Fattahi, P. (2017). “An Analytical and comparative
approach for using Meta heuristic algorithms for job shop scheduling
problems”, Journal of Applied Mathematics - Lahijan Azad
University, 14, 63–76.
[25] Saputro, T., Figueira, G., & Almada-Lobo, B. (2019). “Integration of
Supplier Selection and Inventory Management under Supply
Disruptions”, IFAC-Pap, 52, 2827–2832.
[26] Sawik, T., (2013). “Selection of resilient supply portfolio under
disruption risks”, Omega, Management science and environmental,
41, 259–269.
[27] Shokouhifar, M., & Jalali, A. (2017). Optimized sugeno fuzzy
clustering algorithm for wireless sensor networks. Engineering
applications of artificial intelligence, 60, 16-25.
[28] Shokouhifar, M., Sabbaghi, M. M., & Pilevari, N. (2021). Inventory
management in blood supply chain considering fuzzy supply/demand
uncertainties and lateral transshipmet. Transfusion and Apheresis
Science, 103103.
[29] Shokouhifar, M. (2021). FH-ACO: Fuzzy heuristic-based ant colony
optimization for joint virtual network function placement and
routing. Applied Soft Computing, 107, 107401.
[30] Sörensen, K. (2015). Metaheuristics—the metaphor exposed.
International Transactions in Operational Research, 22(1), 3-18.
