Designing a Sustainable Reverse Logistics Network Considering the Conditional Value at Risk and Uncertainty of Demand under Different Quality and Market Scenarios

Document Type : Original Article

Authors

Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

In recent years, regarding the issues such as lack of natural resources, government laws, environmental concerns and social responsibility reverse and closed-loop supply chains has been in the center of attention of researchers and decision-makers. Then, in this paper, a multi-objective multi-product multi-period mathematical model is presented in the sustainable closed-loop supply chain to locate distribution, collection, recycling, and disposal centers, considering the risk criterion. Conditional value at risk is used as the criterion of risk evaluation. The objectives of this research are to minimize the costs of the chain, reducing the adverse environmental effects and social responsibility in order to maximize job opportunities. Uncertainty in demand and demand-dependent parameters are modeled and determined by the fuzzy inference system. The proposed model has been solved using multi objective particle swarm optimization algorithm (MOPSO) approach and the results have been compared with Epsilon constraint method. Sensitivity analysis was performed on the problem parameters and the efficiency of the studied methods was investigated.

Keywords


1.     Karampour, M. M., Hajiaghaei-Keshteli, M., Fathollahi-Fard, A. M., and Tian, G. “Metaheuristics for a bi-objective green vendor managed inventory problem in a two-echelon supply chain network.” Scientia Iranica, (2020). (In press) https://doi.org/10.24200/sci.2020.53420.3228
2.     Zailani, S., Iranmanesh, M., Foroughi, B., Kim, K., and Hyun, S. S. “Effects of supply chain practices, integration and closed-loop supply chain activities on cost-containment of biodiesel.” Review of Managerial Science, (2019), 1–21. https://doi.org/10.1007/s11846-019-00332-9
3.     Fathollahi-Fard, A. M., Ranjbar-Bourani, M., Cheikhrouhou, N., and Hajiaghaei-Keshteli, M. “Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system.” Computers and Industrial Engineering, Vol. 137, (2019), 106103. https://doi.org/10.1016/j.cie.2019.106103
4.     Yavari, M., and Zaker, H. “Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks.” Computers and Chemical Engineering, Vol. 134, (2020), 106680. https://doi.org/10.1016/j.compchemeng.2019.106680
5.     Sahebjamnia, N., Goodarzian, F., and Hajiaghaei-Keshteli, M. “Optimization of multi-period three-echelon citrus supply chain problem.” Journal of Optimization in Industrial Engineering, Vol. 13, No. 1, (2020), 39–53. https://doi.org/10.22094/JOIE.2017.728.1463
6.     Ghasemi, P., and Talebi Brijani, E. “An Integrated FAHP-PROMETHEE Approach For Selecting The Best Flexible Manufacturing System | Ghasemi | European Online Journal of Natural and Social Sciences.” European Online Journal of Natural and Social Sciences, Vol. 3, No. 4, (2014), 1137–1150. Retrieved from http://european-science.com/eojnss/article/view/815
7.     Mehranfar, N., Hajiaghaei-Keshteli, M., and Fathollahi-Fard, A. M. “A Novel Hybrid Whale Optimization Algorithm to Solve a Production-Distribution Network Problem Considering Carbon Emissions.” International Journal of Engineering, Transactions C: Aspects, Vol. 32, No. 12, (2019), 1781–1789. https://doi.org/10.5829/ije.2019.32.12c.11
8.     Goodarzian, F., and Hosseini-Nasab, H. “Applying a fuzzy multi-objective model for a production–distribution network design problem by using a novel self-adoptive evolutionary algorithm.” International Journal of Systems Science: Operations and Logistics, (2019). https://doi.org/10.1080/23302674.2019.1607621
9.     Shirazi, H., Kia, R., and Ghasemi, P. “Ranking of hospitals in the case of COVID-19 outbreak: A new integrated approach using patient satisfaction criteria.” International Journal of Healthcare Management, (2020), 1–13. https://doi.org/10.1080/20479700.2020.1803622
10.   Babaeinesami, A., and Ghasemi, P. “Ranking of hospitals: A new approach comparing organizational learning criteria.” International Journal of Healthcare Management, (2020). https://doi.org/10.1080/20479700.2020.1728923
11.   Zhen, L., Huang, L., and Wang, W. “Green and sustainable closed-loop supply chain network design under uncertainty.” Journal of Cleaner Production, Vol. 227, (2019), 1195–1209. https://doi.org/10.1016/j.jclepro.2019.04.098
12.   Soleimani, H., Govindan, K., Saghafi, H., and Jafari, H. “Fuzzy multi-objective sustainable and green closed-loop supply chain network design.” Computers and Industrial Engineering, Vol. 109, (2017), 191–203. https://doi.org/10.1016/j.cie.2017.04.038
13.   Giri, B. C., Mondal, C., and Maiti, T. “Analysing a closed-loop supply chain with selling price, warranty period and green sensitive consumer demand under revenue sharing contract.” Journal of Cleaner Production, Vol. 190, (2018), 822–837. https://doi.org/10.1016/j.jclepro.2018.04.092
14.   Samuel, C. N., Venkatadri, U., Diallo, C., and Khatab, A. “Robust closed-loop supply chain design with presorting, return quality and carbon emission considerations.” Journal of Cleaner Production, Vol. 247, (2020), 119086. https://doi.org/10.1016/j.jclepro.2019.119086
15.   Mohammed, A., and Wang, Q. “The fuzzy multi-objective distribution planner for a green meat supply chain.” International Journal of Production Economics, Vol. 184, (2017), 47–58. https://doi.org/10.1016/j.ijpe.2016.11.016
16.   Aziziankohan, A., Jolai, F., Khalilzadeh, M., Soltani, R., and Tavakkoli-Moghaddam, R. “Green supply chain management using the queuing theory to handle congestion and reduce energy consumption and emissions from supply chain transportation fleet.” Journal of Industrial Engineering and Management, Vol. 10, No. 2Special Issue, (2017), 213–236. https://doi.org/10.3926/jiem.2170
17.   Yang, D., and Xiao, T. “Pricing and green level decisions of a green supply chain with governmental interventions under fuzzy uncertainties.” Journal of Cleaner Production, Vol. 149, (2017), 1174–1187. https://doi.org/10.1016/j.jclepro.2017.02.138
18.   Mirmohammadi, S. H., and Sahraeian, R. “A novel sustainable closed-loop supply chain network design by considering routing and quality of products.” International Journal of Engineering, Transactions B: Applications, Vol. 31, No. 11, (2018), 1918–1928. https://doi.org/10.5829/ije.2018.31.11b.16
19.   Fathollahi-Fard, A. M., Ahmadi, A., Goodarzian, F., and Cheikhrouhou, N. “A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment.” Applied Soft Computing Journal, Vol. 93, (2020), 106385. https://doi.org/10.1016/j.asoc.2020.106385
20.   Baryannis, G., Validi, S., Dani, S., and Antoniou, G. “Supply chain risk management and artificial intelligence: state of the art and future research directions.” International Journal of Production Research. Vol. 57, No. 7, (2019), 2179-2202. https://doi.org/10.1080/00207543.2018.1530476
21.   Jiang, G., Wang, Q., Wang, K., Zhang, Q., and Zhou, J. “A Novel Closed-Loop Supply Chain Network Design Considering Enterprise Profit and Service Level.” Sustainability, Vol. 12, No. 544, (2020), 1–21. https://doi.org/10.3390/su12020544
22.   Ghasemi, P., Khalili-Damghani, K., Hafezolkotob, A., and Raissi, S. “Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning.” Applied Mathematics and Computation, Vol. 350, (2019), 105–132. https://doi.org/10.1016/j.amc.2018.12.061
23.   He, R., Xiong, Y., and Lin, Z. “Carbon emissions in a dual channel closed loop supply chain: the impact of consumer free riding behavior.” Journal of Cleaner Production, Vol. 134, No. Part A, (2016), 384–394. https://doi.org/10.1016/j.jclepro.2016.02.142
24.   Ghasemi, P., Khalili-Damghani, K., Hafezalkotob, A., and Raissi, S. “Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake).” Socio-Economic Planning Sciences, Vol. 71, (2020), 100745. https://doi.org/10.1016/j.seps.2019.100745
25.   Soleimani, H., and Govindan, K. “Reverse logistics network design and planning utilizing conditional value at risk.” European Journal of Operational Research, Vol. 237, No. 2, (2014), 487–497. https://doi.org/10.1016/j.ejor.2014.02.030
26.   Polo, A., Peña, N., Muñoz, D., Cañón, A., and Escobar, J. W. “Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria.” Omega (United Kingdom), Vol. 88, (2019), 110–132. https://doi.org/10.1016/j.omega.2018.09.003
27.   Jeihoonian, M., Kazemi Zanjani, M., and Gendreau, M. “Closed-loop supply chain network design under uncertain quality status: Case of durable products.” International Journal of Production Economics, Vol. 183, (2017), 470–486. https://doi.org/10.1016/j.ijpe.2016.07.023
28.   Aras, N., Aksen, D., and Gönül Tanuǧur, A. “Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles.” European Journal of Operational Research, Vol. 191, No. 3, (2008), 1223–1240. https://doi.org/10.1016/j.ejor.2007.08.002
29.   Das, K., and Chowdhury, A. H. “Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning.” International Journal of Production Economics, Vol. 135, No. 1, (2012), 209–221. https://doi.org/10.1016/j.ijpe.2011.07.010
30.   Asgari, N., Nikbakhsh, E., Hill, A., and Farahani, R. Z. “Supply chain management 1982-2015: A review.” IMA Journal of Management Mathematics, Vol. 27, No. 3, (2016), 353-379. https://doi.org/10.1093/imaman/dpw004
31.   Liu, X., Tian, G., Fathollahi-Fard, A. M., and Mojtahedi, M. “Evaluation of ship’s green degree using a novel hybrid approach combining group fuzzy entropy and cloud technique for the order of preference by similarity to the ideal solution theory.” Clean Technologies and Environmental Policy, Vol. 22, No. 2, (2020), 493–512. https://doi.org/10.1007/s10098-019-01798-7
32.   Garg, K., Kannan, D., Diabat, A., and Jha, P. C. “A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design.” Journal of Cleaner Production, Vol. 100, (2015), 297–314. https://doi.org/10.1016/j.jclepro.2015.02.075
33.   Iqbal, M. W., Kang, Y., and Jeon, H. W. “Zero waste strategy for green supply chain management with minimization of energy consumption.” Journal of Cleaner Production, Vol. 245, (2020), 118827. https://doi.org/10.1016/j.jclepro.2019.118827
34.   Wang, T., and Li, Q. “Endogenetic Risk Analysis of the Replenishment Supply Chain in Large Fresh Supermarket.” World Scientific Research Journal, Vol. 6, No. 1, (2020), 165–172. https://doi.org/10.6911/WSRJ.202001_6(1).0024
35.   Safaeian, M., Fathollahi-Fard, A. M., Tian, G., Li, Z., and Ke, H. “A multi-objective supplier selection and order allocation through incremental discount in a fuzzy environment.” Journal of Intelligent and Fuzzy Systems, Vol. 37, No. 1, (2019), 1435–1455. https://doi.org/10.3233/JIFS-182843
36.   Wang, J., Jiang, H., and Yu, M. “Pricing decisions in a dual-channel green supply chain with product customization.” Journal of Cleaner Production, Vol. 247, (2020), 119101. https://doi.org/10.1016/j.jclepro.2019.119101
37.   Fathollahi-Fard, A. M., Govindan, K., Hajiaghaei-Keshteli, M., and Ahmadi, A. “A green home health care supply chain: New modified simulated annealing algorithms.” Journal of Cleaner Production, Vol. 240, (2019), 118200. https://doi.org/10.1016/j.jclepro.2019.118200
38.   Baptista, S., Barbosa-Póvoa, A. P., Escudero, L. F., Gomes, M. I., and Pizarro, C. “On risk management of a two-stage stochastic mixed 0–1 model for the closed-loop supply chain design problem.” European Journal of Operational Research, Vol. 274, No. 1, (2019), 91–107. https://doi.org/10.1016/j.ejor.2018.09.041
39.   Yun, Y., Chuluunsukh, A., and Gen, M. “Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach.” Mathematics, Vol. 8, No. 84, (2020), 1–19. https://doi.org/10.3390/math8010084
40.   Rabbani, M., Hosseini-Mokhallesun, S. A. A., Ordibazar, A. H., and Farrokhi-Asl, H. “A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design.” International Journal of Systems Science: Operations and Logistics, Vol. 7, No. 1, (2020), 60–75. https://doi.org/10.1080/23302674.2018.1506061
41.   Roghanian, E., and Cheraghalipour, A. “Addressing a set of meta-heuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions.” Journal of Cleaner Production, Vol. 239, (2019), 118081. https://doi.org/10.1016/j.jclepro.2019.118081
42.   Taleizadeh, A. A., Alizadeh-Basban, N., and Niaki, S. T. A. “A closed-loop supply chain considering carbon reduction, quality improvement effort, and return policy under two remanufacturing scenarios.” Journal of Cleaner Production, Vol. 232, (2019), 1230–1250. https://doi.org/10.1016/j.jclepro.2019.05.372
43.   Rahimi, M., and Ghezavati, V. “Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste.” Journal of Cleaner Production, Vol. 172, (2018), 1567–1581. https://doi.org/10.1016/j.jclepro.2017.10.240
44.   Khalili-Damghani, K., and Ghasemi, P. “Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains :Fuzzy Mathematical Optimization Approaches.” Industrial Engineering & Management Systems, Vol. 15, No. 2, (2016), 156–172. Retrieved from https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE06699601
45.   Chen, C. L., Yuan, T. W., and Lee, W. C. “Multi-criteria fuzzy optimization for locating warehouses and distribution centers in a supply chain network.” Journal of the Chinese Institute of Chemical Engineers, Vol. 38, No. 5–6, (2007), 393–407. https://doi.org/10.1016/j.jcice.2007.08.001
46.   Petrovic, D., Xie, Y., Burnham, K., and Petrovic, R. “Coordinated control of distribution supply chains in the presence of fuzzy customer demand.” European Journal of Operational Research, Vol. 185, No. 1, (2008), 146–158. https://doi.org/10.1016/j.ejor.2006.12.020
47.   El-Sayed, M., Afia, N., and El-Kharbotly, A. “A stochastic model for forward-reverse logistics network design under risk.” Computers and Industrial Engineering, Vol. 58, No. 3, (2010), 423–431. https://doi.org/10.1016/j.cie.2008.09.040
48.   Pishvaee, M. S., Farahani, R. Z., and Dullaert, W. “A memetic algorithm for bi-objective integrated forward/reverse logistics network design.” Computers and Operations Research, Vol. 37, No. 6, (2010), 1100–1112. https://doi.org/10.1016/j.cor.2009.09.018
49.   Ali, S. S., Paksoy, T., Torğul, B., and Kaur, R. “Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach.” Wireless Networks, Vol. 26, No. 8, (2020), 5759–5782. https://doi.org/10.1007/s11276-019-02246-6
50.   Nezhadroshan, A. M., Fathollahi-Fard, A. M., and Hajiaghaei-Keshteli, M. “A scenario-based possibilistic-stochastic programming approach to address resilient humanitarian logistics considering travel time and resilience levels of facilities.” International Journal of Systems Science: Operations and Logistics, (2020), 1-27. https://doi.org/10.1080/23302674.2020.1769766
51.   Ma, L., Liu, Y., and Liu, Y. “Distributionally robust design for bicycle-sharing closed-loop supply chain network under risk-averse criterion.” Journal of Cleaner Production, Vol. 246, (2020), 118967. https://doi.org/10.1016/j.jclepro.2019.118967
52.   Sunil, B., and Indrani, P. “ICFAI Journal of Supply Chain Management.” ICFAI Journal of Supply Chain Management, Vol. 5, No. 3, (2008), 59–68. Retrieved from https://biblioteca.sagrado.edu/eds/detail?db=bsu&an=35272238
53.   Zhou, Y. ju, Chen, X. hong, and Wang, Z. run. “Optimal ordering quantities for multi-products with stochastic demand: Return-CVaR model.” International Journal of Production Economics, Vol. 112, No. 2, (2008), 782–795. https://doi.org/10.1016/j.ijpe.2007.04.014
54.   Rockafellar, R. T., and Uryasev, S. “Conditional value-at-risk for general loss distributions.” Journal of Banking and Finance, Vol. 26, No. 7, (2002), 1443–1471. https://doi.org/10.1016/S0378-4266(02)00271-6
55.   Goh, M., and Meng, F. “A stochastic model for supply chain risk management using conditional value at risk.” In Managing Supply Chain Risk and Vulnerability: Tools and Methods for Supply Chain Decision Makers (pp. 141–157). Springer London., 2009. https://doi.org/10.1007/978-1-84882-634-2_8
56.   Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., and Tavakkoli-Moghaddam, R. “Red deer algorithm (RDA): a new nature-inspired meta-heuristic.” Soft Computing, Vol. 24, No. 19, (2020), 14637–14665. https://doi.org/10.1007/s00500-020-04812-z
57.   Xia, W., and Wu, Z. “An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems.” Computers and Industrial Engineering, Vol. 48, No. 2, (2005), 409–425. https://doi.org/10.1016/j.cie.2005.01.018
58.   Mohammed, F., Selim, S. Z., Hassan, A., and Syed, M. N. “Multi-period planning of closed-loop supply chain with carbon policies under uncertainty.” Transportation Research Part D: Transport and Environment, Vol. 51, (2017), 146–172. https://doi.org/10.1016/j.trd.2016.10.033
59.   Fazli-Khalaf, M., Mirzazadeh, A., and Pishvaee, M. S. “A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network.” Human and Ecological Risk Assessment, Vol. 23, No. 8, (2017), 2119–2149. https://doi.org/10.1080/10807039.2017.1367644
60.   Fallah-Tafti, A., Vahdatzad, M. A., and Sadegheiyeh, A. “A comprehensive mathematical model for a location-routing-inventory problem under uncertain demand: A numerical illustration in cash-in-transit sector.” International Journal of Engineering, Transactions B: Applications, Vol. 32, No. 11, (2019), 1634–1642. https://doi.org/10.5829/ije.2019.32.11b.15
61.   Ghasemi, P., Khalili-Damghani, K., Hafezolkotob, A., and Raissi, S. “A decentralized supply chain planning model: a case study of hardboard industry.” International Journal of Advanced Manufacturing Technology, Vol. 93, No. 9–12, (2017), 3813–3836. https://doi.org/10.1007/s00170-017-0802-3
62.   Jung, H., and Jeong, S. J. “Managing demand uncertainty through fuzzy inference in supply chain planning.” International Journal of Production Research, Vol. 50, No. 19, (2012), 5415–5429. https://doi.org/10.1080/00207543.2011.631606
63.   Amindoust, A., Ahmed, S., Saghafinia, A., and Bahreininejad, A. “Sustainable supplier selection: A ranking model based on fuzzy inference system.” Applied Soft Computing Journal, Vol. 12, No. 6, (2012), 1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023
64.   Prasad, S., and Sounderpandian, J. “Factors influencing global supply chain efficiency: Implications for information systems.” Supply Chain Management, Vol. 8, No. 3, (2003), 241–250. https://doi.org/10.1108/13598540310484636
65.   Attaran, M., and Attaran, S. “Collaborative supply chain management: The most promising practice for building efficient and sustainable supply chains.” Business Process Management Journal, Vol. 13, No. 3, (2007), 390–404. https://doi.org/10.1108/14637150710752308
66.   Abdi, A., Abdi, A., Fathollahi-Fard, A. M., and Hajiaghaei-Keshteli, M. “A set of calibrated metaheuristics to address a closed-loop supply chain network design problem under uncertainty.” International Journal of Systems Science: Operations and Logistics, (2019). https://doi.org/10.1080/23302674.2019.1610197