A Comparative Study of Meta-heuristic Algorithms in Supply Chain Networks
Subject Areas : Artificial IntelligenceFariba 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
Keywords:
Abstract :
[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.