Abstract
The growing concern for sustainability has forced the researchers and managers to incorporate the environmental and social factors along with the economical factors in the design of supply chains. This paper presents the design and optimization of a multi-objective closed-loop supply chain considering the economical and environmental factors with uncertainty in parameters. The proposed network is modeled as fuzzy multi-objective mixed integer linear programming problem considering multi-customer zones, multi-collection centers, multi-disassembly centers, multi-refurbishing centers, multi-external suppliers, and different product recovery processes; to take care for purchasing cost, transportation cost, processing cost, set-up cost, and capacity constraints simultaneously. The model is solved using an interactive \(\upvarepsilon \)-constraint method. A case example is solved using LINGO 14.0 to demonstrate the significance and applicability of the developed fuzzy optimization model for closed-loop supply chain.
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Abdallah, T., Diabat, A., & Simchi-Levi, D. (2011). Sustainable supply chain design: A closed-loop formulation and sensitivity analysis. Production Planning & Control, 23(2–3), 120–133.
Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39(8), 6782–6791.
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165–4176.
Aras, N., & Aksen, D. (2008). Locating collection centers for distance- and incentive-dependent returns. International Journal of Production Economics, 111(2), 316–333.
Beamon, B. M., & Fernandes, C. (2004). Supply-chain network configuration for product recovery. Production Planning & Control, 15(3), 270–281.
Bernon, M., & Cullen, J. (2007). An integrated approach to managing reverse logistics. International Journal of Logistics Research and Applications, 10(1), 41–56.
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312.
Dehghanian, F., & Mansour, S. (2009). Designing sustainable recovery network of end-of-life products using genetic algorithm. Resources, Conservation and Recycling, 53(10), 559–570.
Demirel, N., & Gökçen, H. (2008). A mixed integer programming model for remanufacturing in reverse logistics environment. The International Journal of Advanced Manufacturing Technology, 39(11), 1197–1206.
Easwaran, G., & Üster, H. (2010). A closed-loop supply chain network design problem with integrated forward and reverse channel decisions. IIE Transactions, 42(11), 779–792.
Ecoinvent Centre, (2010). Ecoinvent Database v2.2 [online]. Swiss Centre for life cycle inventories, Switzerland. http://www.ecoinvent.org/home/
El-Sayed, M., Afia, N., & El-Kharbotly, A. (2010). A stochastic model for forward-reverse logistics network design under risk. Computers & Industrial Engineering, 58(3), 423–431.
Guide, V. D. R, Jr., Jayaraman, V., Srivastava, R., & Benton, W. C. (2000). Supply-chain management for recoverable manufacturing systems. Interfaces, 30(3), 125–142.
Guide, V. D. R., & Van Wassenhove, L. N. (2001). Managing product returns for remanufacturing. Production and Operations Management, 10(2), 142–155.
Harraz, N. A., & Galal, N. M. (2011). Design of sustainable end-of-life vehicle recovery network in Egypt. Ain Shams Engineering Journal, 2(3–4), 211–219.
Ilgin, M. A., & Gupta, S. M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of Environmental Management, 91(3), 563–591.
Jayaraman, V. (2006). Production planning for closed-loop supply chains with product recovery and reuse: An analytical approach. International Journal of Production Research, 44(5), 981–998.
Jiménez, M., Arenas, M., Bilbao, A., & Rodrı’guez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599–1609.
Jindal, A., & Sangwan, K. S. (2013). Development of an interpretive structural model of drivers for reverse logistics implementation in Indian industry. International Journal of Business Performance and Supply Chain Modelling, 5(4), 325–342.
Jindal, A., & Sangwan, K. S. (2014). Closed loop supply chain network design and optimisation using fuzzy mixed integer linear programming model. International Journal of Production Research, 52(14), 4156–4173.
Jindal, A., Sangwan, K. S., & Saxena, S. (2015). Network design and optimization for multi-product, multi-time, multi-echelon closed-loop supply chain under uncertainty. Procedia CIRP, 29(0), 656–661.
Kannan, D., Diabat, A., Alrefaei, M., Govindan, K., & Yong, G. (2012). A carbon footprint based reverse logistics network design model. Resources, Conservation and Recycling, 67(0), 75–79.
Kannan, G., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603–626.
Kaya, O., Bagci, F., & Turkay, M. (2014). Planning of capacity, production and inventory decisions in a generic reverse supply chain under uncertain demand and returns. International Journal of Production Research, 52(1), 270–282.
Kim, K., Song, I., Kim, J., & Jeong, B. (2006). Supply planning model for remanufacturing system in reverse logistics environment. Computers and Industrial Engineering, 51(2), 279–287.
Ko, H. J., & Evans, G. W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, 34(2), 346–366.
Lai, Y. J., & Hwang, C. L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121–133.
Lee, D.-H., & Dong, M. (2008). A heuristic approach to logistics network design for end-of-lease computer products recovery. Transportation Research Part E: Logistics and Transportation Review, 44(3), 455–474.
Lee, D.-H., Dong, M., & Bian, W. (2010). The design of sustainable logistics network under uncertainty. International Journal of Production Economics, 128(1), 159–166.
Lee, J.-E., Gen, M., & Rhee, K.-G. (2009). Network model and optimization of reverse logistics by hybrid genetic algorithm. Computers & Industrial Engineering, 56(3), 951–964.
Li, X., Li, Y., & Cai, X. (2015). Remanufacturing and pricing decisions with random yield and random demand. Computers & Operations Research, 54(0), 195–203.
Li, Y., Chen, J., & Cai, X. (2006). Uncapacitated production planning with multiple product types, returned product remanufacturing, and demand substitution. OR Spectrum, 28(1), 101–125.
Li, Y., Chen, J., & Cai, X. (2007). Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing. International Journal of Production Economics, 105(2), 301–317.
Lieckens, K., & Vandaele, N. (2007). Reverse logistics network design with stochastic lead times. Computers & Operations Research, 34(2), 395–416.
Listeş, O. (2007). A generic stochastic model for supply-and-return network design. Computers & Operations Research, 34(2), 417–442.
Listeş, O., & Dekker, R. (2005). A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 160(1), 268–287.
Lu, Z., & Bostel, N. (2007). A facility location model for logistics systems including reverse flows: The case of remanufacturing activities. Computers & Operations Research, 34(2), 299–323.
Mahmoudzadeh, M., Mansour, S., & Karimi, B. (2013). To develop a third-party reverse logistics network for end-of-life vehicles in Iran. Resources, Conservation and Recycling, 78(0), 1–14.
Mansour, S., & Zarei, M. (2008). A multi-period reverse logistics optimisation model for end-of-life vehicles recovery based on EU directive. International Journal of Computer Integrated Manufacturing, 21(7), 764–777.
Mavrotas, G. (2009). Effective implementation of the \(\varepsilon \)-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455–465.
Mehrbod, M., Tu, N., Miao, L., & Wenjing, D. (2012). Interactive fuzzy goal programming for a multi-objective closed-loop logistics network. Annals of Operations Research, 201(1), 367–381.
Min, H., Jeung Ko, H., & Seong Ko, C. (2006a). A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega, 34(1), 56–69.
Min, H., Ko, C. S., & Ko, H. J. (2006b). The spatial and temporal consolidation of returned products in a closed-loop supply chain network. Computers & Industrial Engineering, 51(2), 309–320.
Mitra, S. (2012). Inventory management in a two-echelon closed-loop supply chain with correlated demands and returns. Computers & Industrial Engineering, 62(4), 870–879.
Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74–97.
Mutha, A., & Pokharel, S. (2009). Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers and Industrial Engineering, 56(1), 334–346.
Nikolaou, I. E., Evangelinos, K. I., & Allan, S. (2013). A reverse logistics social responsibility evaluation framework based on the triple bottom line approach. Journal of Cleaner Production, 56(0), 173–184.
Özceylan, E., & Paksoy, T. (2013a). A mixed integer programming model for a closed-loop supply-chain network. International Journal of Production Research, 51(3), 718–734.
Özceylan, E., & Paksoy, T. (2013b). Fuzzy multi-objective linear programming approach for optimising a closed-loop supply chain network. International Journal of Production Research, 51(8), 2443–2461.
Özkır, V., & Başlıgil, H. (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment. Journal of Cleaner Production, 41(0), 114–125.
Pati, R. K., Vrat, P., & Kumar, P. (2008). A goal programming model for paper recycling system. Omega, 36(3), 405–417.
Pishvaee, M. S., Farahani, R. Z., & Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Operations Research, 37(6), 1100–1112.
Pishvaee, M. S., Jolai, F., & Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems, 28(4), 107–114.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433–3446.
Qiang, Q., Ke, K., Anderson, T., & Dong, J. (2013). The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega, 41(2), 186–194.
Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European Journal of Operational Research, 179(3), 1063–1077.
Sarkis, J., Helms, M. M., & Hervani, A. A. (2010). Reverse logistics and social sustainability. Corporate Social Responsibility and Environmental Management, 17(6), 337–354.
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.
Soleimani, H., Seyyed-Esfahani, M., & Kannan, G. (2013). Incorporating risk measures in closed-loop supply chain network design. International Journal of Production Research, 52(6), 1843–1867.
Üster, H., Easwaran, G., Akçali, E., & Çetinkaya, S. (2007). Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model. Naval Research Logistics (NRL), 54(8), 890–907.
Wang, H.-F., & Huang, Y.-S. (2013). A two-stage robust programming approach to demand-driven disassembly planning for a closed-loop supply chain system. International Journal of Production Research, 51(8), 2414–2432.
Wang, I. L., & Wen-Cheng, Y. (2007). Fast heuristics for designing integrated e-waste reverse logistics networks. Electronics Packaging Manufacturing, IEEE Transactions on, 30(2), 147–154.
Wei, C., Li, Y., & Cai, X. (2011). Robust optimal policies of production and inventory with uncertain returns and demand. International Journal of Production Economics, 134(2), 357–367.
Winkler, H. (2011). Closed-loop production systems–A sustainable supply chain approach. CIRP Journal of Manufacturing Science and Technology, 4(3), 243–246.
Zeballos, L. J., Méndez, C. A., Barbosa-Povoa, A. P., & Novais, A. Q. (2014). Multi-period design and planning of closed-loop supply chains with uncertain supply and demand. Computers & Chemical Engineering, 66, 151–164.
Zimmermann, H. J. (2001). Fuzzy set theory and its applications : Springer.
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Jindal, A., Sangwan, K.S. Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors. Ann Oper Res 257, 95–120 (2017). https://doi.org/10.1007/s10479-016-2219-z
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DOI: https://doi.org/10.1007/s10479-016-2219-z