Repairable spare part supply chain: A hybrid priority-based particle swarm approach

Document Type : Research Paper

Authors

1 School of Management and Economics, Science and Research Branch, Islamic Azad University Iran University of Science and Technology, Tehran, Iran

2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

The industry life highly depends on spare parts since it is vital to perform maintenance operations, especially in strategic industries. The expensive and low-demand spare parts are a must for the continuation of the production; therefore, they are held in warehouses to meet unexpected demand. These spare parts cause high inventory costs also they require human resources, energy, and budget for the repair operations. It is important to point out that separate optimization of decisions in spare part supply chain leads to sub-optimality so, an integrated mathematical model can outperform a routine model. In this paper, we present a network design and planning model that is integrated with the METRIC model (Multi-Echelon Technique for Recoverable Item Control) that formulates inventory management decisions of the repairable spare parts. This model covers different decisions such as supplier order assignment, stock level in warehouses, flows among the facilities, and location of facilities. Due to the np-hardness of the problem, a hybrid approach is presented that incorporates heuristic and meta-heuristic methods. This approach is used to solve the proposed model that has been never applied in previous researches for such a model.

Keywords

Main Subjects


Ahmadi kurd, H., Yaghobi, S., & Taghan zadeh, A. hakim. (1396). A robust optimization Model for designing reverse water network for Agricultural comsumption (Case Study: Tehran Province). 28(4), 633–647.
Alborzi, M. (2019). Management information system (2nd ed., Vol. 1). Andishehaye goharbar.
Aras, N., Aksen, D., & Tanu─čur, A. G. (2008). Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles. European Journal of Operational Research, 191(3), 1223–1240.
Babaveisi, V., Paydar, M. M., & Safaei, A. S. (2018). Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms. Journal of Industrial Engineering International, 14(2), 305–326. https://doi.org/10.1007/s40092-017-0217-7
Carrasco-Gallego, R., Ponce-Cueto, E., & Dekker, R. (2012). Closed-loop supply chains of reusable articles: A typology grounded on case studies. International Journal of Production Research, 50(19), 5582–5596.
Driessen, M. A. (2018). Integrated capacity planning and inventory control for repairable spare parts.
Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942–1948.
Fathollahi-Fard, A. M., Ahmadi, A., & Al-e-Hashem, S. M. J. M. (2020). Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty. Journal of Environmental Management, 275, 111277. https://doi.org/10.1016/j.jenvman.2020.111277
Fonseca, M. C., García-Sánchez, Á., Ortega-Mier, M., & Saldanha-da-Gama, F. (2010). A stochastic bi-objective location model for strategic reverse logistics. Top, 18(1), 158–184.
Frandsen, C. S., Nielsen, M. M., Chaudhuri, A., Jayaram, J., & Govindan, K. (2020). In search for classification and selection of spare parts suitable for additive manufacturing: A literature review. International Journal of Production Research, 58(4), 970–996. https://doi.org/10.1080/00207543.2019.1605226
González-Varona, J. M., Poza, D., Acebes, F., Villafáñez, F., Pajares, J., & López-Paredes, A. (2020). New Business Models for Sustainable Spare Parts Logistics: A Case Study. Sustainability, 12(8), 3071.
Hatefi, S. M., Jolai, F., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2015). Reliable design of an integrated forward-revere logistics network under uncertainty and facility disruptions: A fuzzy possibilistic programing model. KSCE Journal of Civil Engineering, 19(4), 1117–1128.
He, X., & Hu, W. (2014). Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network. The Scientific World Journal, 2014, 195053. https://doi.org/10.1155/2014/195053
Hora, M. E. (1987). The unglamorous game of managing maintenance. Business Horizons, 30(3), 67–75.
Jain, S., & Raghavan, N. R. S. (2009). A queuing approach for inventory planning with batch ordering in multi-echelon supply chains. Central European Journal of Operations Research, 17(1), 95–110. https://doi.org/10.1007/s10100-008-0077-8
Jayaraman, V., Guide, V. D. R., & Srivastava, R. (1999). A closed-loop logistics model for remanufacturing. Journal of the Operational Research Society, 50(5), 497–508. https://doi.org/10.1057/palgrave.jors.2600716
Karamouzian, A., Teimoury, E., & Modarres, M. (2011). A model for admission control of returned products in a remanufacturing facility using queuing theory. The International Journal of Advanced Manufacturing Technology, 54(1–4), 403–412.
Karim, R., & Nakade, K. (2021). An integrated location-inventory model for a spare part’s supply chain considering facility disruption risk and CO2 emission. Journal of Industrial Engineering and Management, 14(2), 87–119.
Kim, J., Chung, B. D., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314–1328. https://doi.org/10.1016/j.jclepro.2018.06.157
Kosanoglu, F., Turan, H. H., & Atmis, M. (2018). A Simulated Annealing Algorithm for Integrated Decisions on Spare Part Inventories and Cross-Training Policies in Repairable Inventory Systems. Proceedings of International Conference on Computers and Industrial Engineering, 1–14.
Paydar, M. M., Babaveisi, V., & Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk. Computers & Chemical Engineering, 104, 38–55. https://doi.org/10.1016/j.compchemeng.2017.04.005
Prasanna Venkatesan, S., & Kumanan, S. (2012). A multi-objective discrete particle swarm optimisation algorithm for supply chain network design. International Journal of Logistics Systems and Management, 11(3), 375–406.
Qin, X., Jiang, Z.-Z., Sun, M., Tang, L., & Liu, X. (2021). Repairable spare parts provisioning for multiregional expanding fleets of equipment under performance-based contracting. Omega, 102, 102328.
Rabbani, M., Hosseini-Mokhallesun, S. A. A., Ordibazar, A. H., & Farrokhi-Asl, H. (2020). A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design. International Journal of Systems Science: Operations & Logistics, 7(1), 60–75. https://doi.org/10.1080/23302674.2018.1506061
Sadeghi, A., Mina, H., & Bahrami, N. (2020). A mixed integer linear programming model for designing a green closed-loop supply chain network considering location-routing problem. International Journal of Logistics Systems and Management, 36(2), 177–198.
Sarrafha, K., Kazemi, A., & Alinejad, A. (1394). Designing and optimizing the integrated production-distribution planning problem in a multi-level supply chain network: A multi-objective evolutionary approach. 26(3), 283–298.
Sasikumar, P., Kannan, G., & Haq, A. N. (2010). A multi-echelon reverse logistics network design for product recovery—A case of truck tire remanufacturing. The International Journal of Advanced Manufacturing Technology, 49(9–12), 1223–1234.
Talbi, E.-G. (2009). Metaheuristics: From design to implementation (Vol. 74). John Wiley & Sons.
Topan, E., & van der Heijden, M. C. (2020). Operational level planning of a multi-item two-echelon spare parts inventory system with reactive and proactive interventions. European Journal of Operational Research, 284(1), 164–175. https://doi.org/10.1016/j.ejor.2019.12.022
Tosarkani, B. M., & Amin, S. H. (2019). An environmental optimization model to configure a hybrid forward and reverse supply chain network under uncertainty. Computers & Chemical Engineering, 121, 540–555. https://doi.org/10.1016/j.compchemeng.2018.11.014
Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., & Baboli, A. (2012). Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model. Transportation Research Part E: Logistics and Transportation Review, 48(6), 1152–1168. https://doi.org/10.1016/j.tre.2012.06.002
Wang, F., & Lin, L. (2021). Spare parts supply chain network modeling based on a novel scale-free network and replenishment path optimization with Q learning. Computers & Industrial Engineering, 157, 107312.
Wilson, W. (2020). What’s the real cost of spare parts inventory? Resource Library. https://www.lce.com/Whats-the-real-cost-of-spare-parts-inventory-1189.html
Zhao, Y., Shi, Y., & Karimi, H. R. (2012). Entry-item-quantity-ABC analysis-based multitype cigarette fast sorting system. Mathematical Problems in Engineering, 2012.
Volume 14, Issue 1 - Serial Number 1
January 2021
Pages 187-204
  • Receive Date: 11 July 2021
  • Revise Date: 15 October 2021
  • Accept Date: 30 October 2021
  • First Publish Date: 30 October 2021