A Bi-objective Mathematical Model for Closed-loop Supply Chain Network Design Problem

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

In this paper, a bi-objective mixed-integer linear optimization model for Closed-loop Supply Chain Network Design Problem (CLSCND) is developed. The proposed model includes both the forward and reverse directions and includes different types of facilities, namely, manufacturing/remanufacturing centers, warehouses, and disassembly centers. The first objective function tried to minimize the total cost of the supply chain, while the second one was aimed at maximizing the responsiveness of the network in both forward and reverse directions, simultaneously. To solve the proposed bi-objective model, an augmented ε-constraint method was implemented by which a set of Pareto-optimal solutions for the problem were generated. An illustrative numerical example is given in the study to show the applicability and efficiency of the presented optimization model.

Keywords


Babazadeh, R., Razmi, J., & Ghodsi, R. (2013). Facility location in responsive and flexible supply chain network design (SCND) considering outsourcing. International Journal of Operational Research, 17(3), 295–310. https://doi.org/10.1504/IJOR.2013.054437

Chen, H. K., & Chou, H. W. (2006). Reverse supply chain network design problem (pp. 42–49). Retrieved from https://www.scopus.com

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. https://doi.org/10.1007/s00170-007-1290-7

Du, Y., Xie, L., Liu, J., Wang, Y., Xu, Y., & Wang, S. (2014). Multi-objective optimization of reverse osmosis networks by lexicographic optimization and augmented epsilon constraint method. Desalination, 333(1), 66–81. https://doi.org/10.1016/j.desal.2013.10.028

Fazli-khalaf, M., Mirzazadeh, A., & Pishvaee, M. S. (2017). Human and Ecological Risk Assessment : An International A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network. Human and Ecological Risk Assessment, 23(8), 2119–2149. https://doi.org/10.1080/10807039.2017.1367644

Fleischman, M., Beullens, P., Bloemhof-Ruwaard, J. M., & Wassenhove, L. N. (2009). The impact of product recovery on logistics network design. Production and Operations Management, 10(2), 156–173. https://doi.org/10.1111/j.1937-5956.2001.tb00076.x

Ghassemi, A., Asl-Najafi, J., & Yaghoubi, S. (2018). A dynamic bi-objective closed-loop supply chain network design considering supplier selection and remanufacturer subcontractors. Uncertain Supply Chain Management, 6(2), 117–134. https://doi.org/10.5267/j.uscm.2017.9.001

Ghayebloo, S., Jafar, M., & Venkatadri, U. (2015). Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products : The impact of parts reliability and product greenness on the recovery network. Journal of Manufacturing Systems, 36, 76–86. https://doi.org/10.1016/j.jmsy.2015.02.011

Ghomi-avili, M., Gholamreza, S., Naeini, J., Tavakkoli-moghaddam, R., & Jabbarzadeh, A. (2018). A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions. Journal of Cleaner Production, 188, 425–442. https://doi.org/10.1016/j.jclepro.2018.03.273

Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), 108–141. https://doi.org/10.1016/j.ejor.2017.04.009

Haddadsisakht, A., & Ryan, S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International Journal of Production Economics, 195, 118–131. https://doi.org/10.1016/j.ijpe.2017.09.009

Kadambala, D. K., Subramanian, N., Tiwari, M. K., Abdulrahman, M., & Liu, C. (2017). Closed loop supply chain networks: Designs for energy and time value efficiency. International Journal of Production Economics, 183, 382–393. https://doi.org/10.1016/j.ijpe.2016.02.004

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(3), 655–670. https://doi.org/10.1016/j.apm.2009.06.021

Khalilpourazari, S., & Arshadi Khamseh, A. (2017). Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Annals of Operations Research, 1–39. https://doi.org/10.1007/s10479-017-2588-y

Krikke, H. R., van Harten, A., & Schuur, P. C. (1999). Business case Océ: Reverse logistic network re-design for copiers. OR Spectrum, 21(3), 381–409. https://doi.org/10.1007/s002910050095

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. https://doi.org/10.1016/j.cie.2008.09.021

Masoudipour, E., Amirian, H., & Sahraeian, R. (2017). A novel closed-loop supply chain based on the quality of returned products. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2017.03.067

Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455–465. https://doi.org/10.1016/j.amc.2009.03.037

Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652–9669. https://doi.org/10.1016/j.amc.2013.03.002

Min, H., & Ko, H. (2008). The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. International Journal of Production Economics, 113, 176–192.

Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2016). Green supply chain design: a mathematical modelling approach based on a multi-objective optimization model. International Journal of Production Economics, 183(Cl). https://doi.org/http://dx.doi.org/10.1016/j.ijpe.2016.08.028

Özceylan, E., & Paksoy, T. (2013). A Mixed Integer Programming Model for a Closed-loop Supply-chain Network. International Journal of Production Research, 51(3), 718–734.

Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 161(20), 2668–2683. https://doi.org/10.1016/j.fss.2010.04.010

Pishvaee, Mir Saman, Farahani, R. Z., & Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers and Operations Research, 37(6), 1100–1112. https://doi.org/10.1016/j.cor.2009.09.018

Pishvaee, Mir Saman, Jolai, F., & Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems, 28(4), 107–114. https://doi.org/10.1016/j.jmsy.2010.05.001

Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37(1–2), 328–344. https://doi.org/10.1016/j.apm.2012.02.032

Ramezani, M., Kimiagari, A. M., & Karimi, B. (2014). Closed-loop supply chain network design: A financial approach. Applied Mathematical Modelling, 38(15–16), 4099–4119. https://doi.org/10.1016/j.apm.2014.02.004

Saffar, M. M., Shakouri, H., & Razmi, J. (2015). A new multi objective optimization model for designing a green supply chain network under uncertainty. International Journal of Industrial Engineering Computations, 6, 15–32. https://doi.org/10.5267/j.ijiec.2014.10.001

Salema, M. I. G., Póvoa, A. P. B., & Novais, A. Q. (2009). A strategic and tactical model for closed-loop supply chains. OR Spectrum, 31(3), 573–599. https://doi.org/10.1007/s00291-008-0160-5

Sasikumar, P., & Kannan, G. (2008). Issues in reverse supply chains, part I: end‐of‐life product recovery and inventory management – an overview. International Journal of Sustainable Engineering, 1(3), 154–172. https://doi.org/10.1080/19397030802433860

Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662–673. https://doi.org/10.1016/j.jclepro.2015.10.074

Tavakkoli-moghaddam, R., Sadri, S., Pourmohammad-zia, N., & Mohammadi, M. (2015). A hybrid fuzzy approach for the closed-loop supply chain network design under uncertainty. Journal of Intelligent & Fuzzy Systems, 28, 2811–2826. https://doi.org/10.3233/IFS-151561

Pazhani, S., Ramkumar, N., Narendran, T. T., & Ganesh, K. (2013). A bi-objective network design model for multi- period , multi-product closed-loop supply chain. Journal of Industrial and Production Engineering, 30(4), 264–280. https://doi.org/10.1080/21681015.2013.830648

Yang, Y., Huang, Z., Qiang, Q. P., & Zhou, G. (2017). A Mathematical Programming Model with Equilibrium Constraints for Competitive Closed-Loop Supply Chain Network Design. Asia-Pacific Journal of Operational Research, 34(05), 1750026. https://doi.org/10.1142/S0217595917500269

Yun, Y. S., Anudari, C., Chen, X., & Hwang, R. (2016). Closed-loop supply chain network model with product recovery, reselling and waste disposal. 46th International Conferences on Computers and Industrial Engineering.