Solving a Multi-Item Supply Chain Network Problem by Three Meta-heuristic Algorithms

Subject Areas : Executive Management

Amir Fatehi Kivi ¹ ( Young researchers and elite clud, Khalkhal branch Islamic azad university, khalkhal, iran, )
Esmaeil Mehdizadeh ² ( Islamic Azad University, Qazvin Branch )
Reza Tavakkoli-Moghaddam ³ ( North Karegar Street School of Industrial Engineering, College of Engineering, University of Tehran )
Seyed Esmaeil Najafi ⁴ ( Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran )

Received: 2019-04-22 Accepted : 2020-06-01 Published : 2021-12-01

Keywords: Harmony search, Genetic Algorithm, Supply chain network design, Tabu search, Multi-mode demand,

Abstract :

The supply chain network design not only assists organizations production process (e.g.,plan, control and execute a product’s flow) but also ensure what is the growing need for companies in a longterm. This paper develops a three-echelon supply chain network problem including multiple plants, multiple distributors, and multiple retailers with amulti-mode demand satisfaction policy inside of production planning and maintenance. The problem is formulated as a mixed-integer linear programming model. Because of its NP-hardness, three meta-heuristic algorithms(i.e., tabu search, harmony search and genetic algorithm) are used to solve the given problem. Also, theTaguchi method is used to choose the best levels of the parameters of the proposedmeta-heuristic algorithms. The results show that HS has abetter solution quality than two other algorithms.

References:

Amorim. P., Gunther, H O., Almada-Lobo. B., (2012). Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics, 138, 89–101.

Ardalan. Z., Karimi. S., Naderi. B., ArshadiKhamseh, A., (2016). Supply chain networks design with multi-mode demand satisfaction policy. Computers and Industrial Engineering, 96, 108-117.

Badri. H., Ghomi. F., Hejazi. T., (2017). A two-stage stochastic programming approach for value-based closed-loop supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 105, 1-17.

Bahrampour. P., Safari. M., Taraghdari. M,B., (2016). Modeling multi-product multi-stage supply chain network design. Procedia Economics and Finance, 36, 70 – 80.

Chandra. P., Fisher. M. L., (1994). Coordination of production and distribution planning. European Journal of Operational Research, 72(3), 503–517.

Dhaenens-Flipo. C., (2000). Spatial decomposition for a multi-facility production and distribution problem, International Journal of Production Economics, 64, 177–186.

Eduardo. F., Andre. A., Enzo. F., Bernd. H., (2017). Operational supply chain planning method for integrating spare parts supply chains and intelligent maintenance systems. IFAC-PapersOnLine, 50(1), 12428-12433.

Fattahi. M., Mahootchi. M., Kannan. G., Husseini. M., (2015). Dynamic supply chain network design with capacity planning and multi-period pricing. Transportation Research Part E: Logistics and Transportation Review, 81,169-202.

Taguchi. G., S. Chowdhury., (2000). Taguchi: Robust Engineering, McGraw-Hill, New York.

Geem. Z., Kim. J., Loganathan. G., (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–8.

Gen. F., Lin. M., Paksoy. T., (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, 51, 196–215.

Glover. F., (1986). Future paths for integer programming and links to artificial intelligence. Computer& Operation Research, 13, 533-549.

Gourdin. E., Labbe. M., Laporte. G., (2000). The uncapacitated facility location problem with client matching. Operations Research, 48(5), 671–85.

Holland. J.H., (1975). Adaptation in natural and artificial systems, Ann Arbor: The University of Michigan Press.

Kalinowski, T., Matthews, J., Waterer. H., (2020). Scheduling of maintenance windows in a mining supply chain rail network. Computers & Operations Research. 115,

Kannan. G., Sasikumar. P., Devika. K., (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied Mathematical Modelling, 34, 655-670.

Kostis. T., Chrissoleon. P., (2016). A design model and a production–distribution and inventory planning model in multi-product supply chain networks. International Journal of Production Research, 54, 6436-6457.

Longinidis. P., Georgiadis. M.C., (2011). Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty, International Journal of Production Economics, 129, 262–276.

LunLi. C., Vairaktarakis. G., (2007).  Coordinating production and distribution of jobs with bundling operations, IIE Transactions, 39, 203–215.

Manzini. R., Bindi. F., (2009). Strategic design and operational management optimization of a multi stage physical distribution system. Transportation Research Part E: Logistics and Transportation Review, 45(6), 915-936.

Mehdizadeh. E., Atashi Abkenar. A., (2014). An integrated aggregate production planning model with two-phase production system and maintenance costs.International Journal of Applied Operational Research, 4, 87-106.

Parkinson. H., Thompson. G., (2003). Analysis and taxonomy of remanufacturing industry practice. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 217(3), 243–256.

Pawar. P.J., Nundurkar. K.N., (2018). Optimization of single supplier multi buyer multi product supply chain system, Procedia Manufacturing, 26, 21–28.

Ruimin. M., Lifei. Y., Maozhu. J., Peiyu. R., (2015). Robust environmental closed-loop supply chain design under uncertainty, Chaos Solitons& Fractals, 89, 195-202.

Sasitharan. D., Lazim. D.M., (2018). Effect of Preventive Maintenance Practices and Supply Chain Management in Improving Manufacturing Performance. International Journal of Innovative Research & Development. 7 (9), 223-226.

Tirkolaee. E.B., Mahmud khani. J., Bourani. M. R., Tavakkoli-Moghaddam. R., (2019). A Self-Learning Particle Swarm Optimization for Robust Multi-Echelon Capacitated Location-Allocation-Inventory Problem. Journal of Advanced Manufacturing Systems, 18, 677-694.

Yao. M.J., Hsu. H.W., (2009). A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs. Optimization and Engineering, 10(2), 219-237.‏

Yeh. R.H., Lo. H.C., Yu. R.Y., (2011). A study of maintenance policies for second-hand products. Computers and Industrial Engineering, 60(3), 438–444.

Zhen. L., Huang. L., Wang. W., (2019). Green and Sustainable Closed-Loop Supply Chain Network Design under Uncertainty. Journal of Cleaner Production. 227, 1195-1209.