A mathematical model for designing sustainable cellular remanufacturing systems

Document Type : Research Paper


Department of Mechanical, Industrial, and Aerospace Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal Quebec Canada


This article proposes an integrated approach towards the design optimization and production planning of cellular manufacturing systems as a part of closed-loop supply chains in an effort to make manufacturing enterprises sustainable. For industrial applications both at the system design and operation stages, a mixed integer linear programming (MILP) model, to integrate the production planning problem in cellular manufacturing systems and the tactical planning of a closed-loop supply chain, has been developed. The cellular manufacturing system in the proposed mathematical model has several features including dynamic cell configuration, multi-period production settings, machine capacity, machine acquisition, machine procurements, and multiple units of identical machines as well as considering different cost parameters such as production cost, operational cost of the machines, and subcontracting cost of the part demands; mainly targeted to be used in industry at the operational level. In addition, several activities such as acquisition, disassembly, setup for disassembly, and disposition of the returned products have been considered on the reverse flow of the closed-loop supply chain of the proposed mathematical model, which would lead to further industrial applications mainly at the integrated design stage of manufacturing and supply chain systems in addition to the potential applications at the operational level.


Main Subjects

Aalaei, A., & Davoudpour, H. (2017). A robust optimization model for cellular manufacturing system into supply chain management. International Journal of Production Economics, 183, 667-679.
Ahkioon, S., Bulgak, A. A., & Bektas, T. (2009). Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration. International Journal of Production Research, 47(6), 1573-1600.
Aljuneidi, T., & Bulgak, A. A. (2020). Carbon footprint for designing reverse logistics network with hybrid manufacturing-remanufacturing systems. Journal of Remanufacturing, 10(2), 107-126.
Aljuneidi, T., & Bulgak, A. A. (2016). Design of cellular manufacturing systems considering dynamic production planning and worker assignments. Journal of Mathematics and System Science, 6, 1-15.
Aljuneidi, T., & Bulgak, A. A. (2016). A mathematical model for designing reconfigurable cellular hybrid manufacturing-remanufacturing systems. The International Journal of Advanced Manufacturing Technology, 87(5), 1585-1596.
Aljuneidi, T., & Bulgak, A. A. (2015). Dynamic Cellular Remanufacturing System (DCRS) Design. International Journal of Industrial and Manufacturing Engineering, 9(5), 859-863.
Badiru, A. B. (2010). The many languages of sustainability: IE's should push for better resource utilization across all fields. Industrial Engineer, 42(11), 30-35.
Baki, M. F., Chaouch, B. A., & Abdul-Kader, W. (2014). A heuristic solution procedure for the dynamic lot sizing problem with remanufacturing and product recovery. Computers & Operations Research, 43, 225-236.
Balakrishnan, J., & Cheng, C. H. (2007). Multi-period planning and uncertainty issues in cellular manufacturing: A review and future directions. European journal of operational research, 177(1), 281-309.
Boukherroub, T., Bouchery, Y., Corbett, C. J., Fransoo, J. C., & Tan, T. (2017). Carbon footprinting in supply chains. In Sustainable Supply Chains (pp. 43-64). Springer, Cham.
Bulgak, A. A., & Bektas, T. (2009). Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. European journal of operational research, 192(2), 414-428.
Chen, M., & Abrishami, P. (2014). A mathematical model for production planning in hybrid manufacturing-remanufacturing systems. The International Journal of Advanced Manufacturing Technology, 71(5-8), 1187-1196.
Chen, M., & Cao, D. (2004). Coordinating production planning in cellular manufacturing environment using Tabu search. Computers & Industrial Engineering, 46(3), 571-588.
Defersha, F. M., & Chen, M. (2008). A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality. European Journal of Operational Research, 187(1), 46-69.
Defersha, F. M., & Chen, M. (2008). A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems. International Journal of Production Research, 46(22), 6389-6413.
Defersha, F. M., & Chen, M. (2006). A comprehensive mathematical model for the design of cellular manufacturing systems. International Journal of Production Economics, 103(2), 767-783.
Demirel, N. Ö., & Gökçen, H. (2008). A mixed integer programming model for remanufacturing in reverse logistics environment. The International Journal of Advanced Manufacturing Technology, 39(11-12), 1197-1206.
Doh, H. H., & Lee, D. H. (2010). Generic production planning model for remanufacturing systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224(1), 159-168.
Egilmez, G., Singh, S., & Ozguner, O. (2017). Cell formation in a cellular manufacturing system under uncertain demand and processing times: a stochastic genetic algorithm approach. International Journal of Services and Operations Management, 26(2), 162-185.
Eguia, I., Racero, J., Guerrero, F., & Lozano, S. (2013). Cell formation and scheduling of part families for reconfigurable cellular manufacturing systems using Tabu search. Simulation, 89(9), 1056-1072.
Fang, C., Liu, X., Pardalos, P. M., Long, J., Pei, J., & Zuo, C. (2017). A stochastic production planning problem in hybrid manufacturing and remanufacturing systems with resource capacity planning. Journal of Global Optimization, 68(4), 851-878.
Forghani, K., & Fatemi Ghomi, S. M. T. (2019). A queuing theory-based approach to designing cellular manufacturing systems. Scientia Iranica, 26(3), 1865-1880.
Garbie, I. H. (2013). DFSME: design for sustainable manufacturing enterprises (an economic viewpoint). International Journal of Production Research, 51(2), 479-503.
Golmohammadi, A., Asadi, A., Amiri, Z., & Behzad, M. (2018). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Management Science Letters, 8(11), 1133-1148.
Guo, J., & Ya, G. (2015). Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products. Computers & Industrial Engineering, 89, 226-234.
Hasanov, P., Jaber, M. Y., & Zolfaghari, S. (2012). Production, remanufacturing and waste disposal models for the cases of pure and partial backordering. Applied Mathematical Modelling, 36(11), 5249-5261.
Kishawy, H. A., Hegab, H., & Saad, E. (2018). Design for sustainable manufacturing: Approach, implementation, and assessment. Sustainability, 10(10), 3604.
Jayakumar, V., & Raju, R. (2010). An adaptive cellular manufacturing system design with routing flexibility and dynamic system reconfiguration. European Journal of Scientific Research, 47(4), 595-611.
Jayal, A. D., Badurdeen, F., Dillon Jr, O. W., & Jawahir, I. S. (2010). Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels. CIRP Journal of Manufacturing Science and Technology, 2(3), 144-152.
Jeihoonian, M., Zanjani, M. K., & Gendreau, M. (2017). Closed-loop supply chain network design under uncertain quality status: Case of durable products. International Journal of Production Economics, 183, 470-486.
Kim, E., Saghafian, S., & Van Oyen, M. P. (2013). Joint control of production, remanufacturing, and disposal activities in a hybrid manufacturing–remanufacturing system. European Journal of Operational Research, 231(2), 337-348.
Koren, Y., & Shpitalni, M. (2010). Design of reconfigurable manufacturing systems. Journal of manufacturing systems, 29(4), 130-141.
Landers, R. G., Min, B. K., & Koren, Y. (2001). Reconfigurable machine tools. CIRP Annals, 50(1), 269-274.
Liu, W., Ma, W., Hu, Y., Jin, M., Li, K., Chang, X., & Yu, X. (2019). Production planning for stochastic manufacturing/remanufacturing system with demand substitution using a hybrid ant colony system algorithm. Journal of Cleaner Production, 213, 999-1010.
Mahdavi, I., Aalaei, A., Paydar, M. M., & Solimanpur, M. (2010). Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment. Computers & Mathematics with Applications, 60(4), 1014-1025.
Mahootchi, M., Forghani, K., & Abdollahi Kamran, M. (2018). A two-stage stochastic model for designing cellular manufacturing systems with simultaneous multiple processing routes and subcontracting. Scientia Iranica, 25(5), 2824-2837.
Mutha, A., & Pokharel, S. (2009). Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers & Industrial Engineering, 56(1), 334-346.
Niakan, F., Baboli, A., Moyaux, T., & Botta-Genoulaz, V. (2016). A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment. Journal of Manufacturing Systems, 38, 46-62.
Purcheck, G. F. (1975). A mathematical classification as a basis for the design of group-technology production cells. Production Engineer, 54(1), 35-48.
Raoofpanah, H., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2019). Solving a new robust green cellular manufacturing problem with environmental issues under uncertainty using Benders decomposition. Engineering Optimization, 51(7), 1229-1250.
Sharifi, S., Chauhan, S. S., & Bhuiyan, N. (2012). Reducing setup time in manufacturing cells in a JIT environment. In Advanced Materials Research (Vol. 488, pp. 1134-1137). Trans Tech Publications Ltd.
Soolaki, M., Arkat, J., & Ahmadizar, F. (2018). Modeling the Trade-off between Manufacturing Cell Design and Supply Chain Design. International Journal of Engineering, 31(4), 640-647.
Tavakkoli-Moghaddam, R., Aryanezhad, M. B., Safaei, N., & Azaron, A. (2005). Solving a dynamic cell formation problem using metaheuristics. Applied Mathematics and Computation, 170(2), 761-780.
Tavakkoli-Moghaddam, R., Safaei, N., & Babakhani, M. (2005, October). Solving a dynamic cell formation problem with machine cost and alternative process plan by memetic algorithms. In International Symposium on Stochastic Algorithms (pp. 213-227). Springer, Berlin, Heidelberg.
Tavakkoli-Moghaddam, R., Safaei, N., & Sassani, F. (2008). A new solution for a dynamic cell formation problem with alternative routing and machine costs using simulated annealing. Journal of the Operational Research Society, 59(4), 443-454.
Wang, J., Zhao, J., & Wang, X. (2011). Optimum policy in hybrid manufacturing/remanufacturing system. Computers & Industrial Engineering, 60(3), 411-419.