Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach


Magnified resource consumption and depletion of natural resources calls for non-flexible or strict regulations and penalties on industrial operations, increased rate of processing and reuse of waste material as a substitute for raw material and political and legal interventions at global scale. Product recovery involves reuse, repair, refurbishing, remanufacturing and materials recycling, requires an efficient network design known as reverse logistic network and offers economical benefits in terms of fewer procurement of raw material, inventory management and less disposal. In current study, a mixed integer linear programming model designed on a multi-stage reverse logistics network for product recovery is proposed which considers different recovery options-product remanufacturing, component reprocessing and material recycling for sustainable outcomes. The model is designed to find optimal solutions for fulfilling demand and revenue needs by focusing on strategic locations for collection centers, reprocessing centers, remanufacturing plants and transportation options and simultaneously achieving sustainability goals. The model is applied on an Indian based manufacturing unit of a Saudi Arabian Industrial Air conditioner manufacturing organization and the case is presented here. The model is converted into a multi-objective programming model in accordance with the importance of each objective suiting the business needs. All relevant objective functions are evaluated using BWM, AHP and FAHP methods to obtain weights for integration into a fuzzy linear programming model which eventually provides three separate results. The model applied has originality and uniqueness for applications to solve multi-objective problems under uncertain environment and tends to strike a balance between economic and environmental objectives. The study provides for a base for further scope covering uncertainty about the amount and quality of returned products and even can be implemented by practitioners and academics for making a significant contribution in improving the efficiency of supply chains.

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  1. 1.

    Tautenhain, C. P., Barbosa-Povoa, A. P., & Nascimento, M. C. (2018). A multi-objective matheuristic for designing and planning sustainable supply chains. Computers & Industrial Engineering, 135, 1203–1223.

    Article  Google Scholar 

  2. 2.

    Zhen, L., Huang, L., & Wang, W. (2019). Green and sustainable closed-loop supply chain network design under uncertainty. Journal of Cleaner Production, 227, 1195–1209.

    Google Scholar 

  3. 3.

    Ramos, T. R. P., Gomes, M. I., & Barbosa-Póvoa, A. P. (2014). Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns. Omega, 48, 60–74.

    Google Scholar 

  4. 4.

    Alfonso-Lizarazo, E. H., Montoya-Torres, J. R., & Gutiérrez-Franco, E. (2013). Modeling reverse logistics process in the agro-industrial sector: The case of the palm oil supply chain. Applied Mathematical Modelling, 37(23), 9652–9664.

    MATH  Google Scholar 

  5. 5.

    Keyvanshokooh, E., Fattahi, M., Seyed-Hosseini, S. M., & Tavakkoli-Moghaddam, R. (2013). A dynamic pricing approach for returned products in integrated forward/reverse logistics network design. Applied Mathematical Modelling, 37(24), 10182–10202.

    MathSciNet  MATH  Google Scholar 

  6. 6.

    Agrawal, S., Singh, R. K., & Murtaza, Q. (2015). A literature review and perspectives in reverse logistics. Resources, Conservation and Recycling, 97, 76–92.

    Google Scholar 

  7. 7.

    Chen, C. L., & Lee, W. C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6–7), 1131–1144.

    Google Scholar 

  8. 8.

    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.

    MathSciNet  MATH  Google Scholar 

  9. 9.

    Mathivathanan, D., Govindan, K., & Haq, A. N. (2017). Exploring the impact of dynamic capabilities on sustainable supply chain firm’s performance using Grey-Analytical Hierarchy Process. Journal of Cleaner Production, 147, 637–653.

    Google Scholar 

  10. 10.

    United Nations Framework Convention on Climate Change; United Nations Framework Convention on Climate Change (UNFCCC). (1992) Climate Change Secretariat: Bonn, Germany.

  11. 11.

    Kyoto Protocol; United Nations Framework Convention on Climate Change (UNFCCC). (1998). Climate Change Secretariat: Bonn, Germany.

  12. 12.

    Copenhagen Accord; United Nations Framework Convention on Climate Change (UNFCCC). (2009). Climate Change Secretariat: Bonn, Germany.

  13. 13.

    Doha Amendment to the Kyoto Protocol; United Nations Framework Convention on Climate Change (UNFCCC). (2012). Climate Change Secretariat: Bonn, Germany.

  14. 14.

    Paris Agreement; United Nations Framework Convention on Climate Change (UNFCCC). (2015). Climate Change Secretariat: Bonn, Germany.

  15. 15.

    Walker, H., & Preuss, L. (2008). Fostering sustainability through sourcing from small businesses: Public sector perspectives. Journal of Cleaner Production, 16(15), 1600–1609.

    Google Scholar 

  16. 16.

    Laari, S., Töyli, J., & Ojala, L. (2017). Supply chain perspective on competitive strategies and green supply chain management strategies. Journal of Cleaner Production, 141, 1303–1315.

    Google Scholar 

  17. 17.

    Depping, V., Grunow, M., van Middelaar, C., & Dumpler, J. (2017). Integrating environmental impact assessment into new product development and processing-technology selection: Milk concentrates as substitutes for milk powders. Journal of Cleaner Production, 149, 1–10.

    Google Scholar 

  18. 18.

    Pietrosemoli, L., & Monroy, C. R. (2013). The impact of sustainable construction and knowledge management on sustainability goals. A review of the Venezuelan renewable energy sector. Renewable and Sustainable Energy Reviews, 27, 683–691.

    Google Scholar 

  19. 19.

    Alemanni, M., Destefanis, F., & Vezzetti, E. (2011). Model-based definition design in the product lifecycle management scenario. International Journal of Advanced Manufacturing Technology, 52, 1–14.

    Google Scholar 

  20. 20.

    Cappa, F., Del Sette, F., Hayes, D., & Rosso, F. (2017). How to deliver open sustainable innovation: An integrated approach for a sustainable marketable product. Sustainability, 8(12), 1341.

    Google Scholar 

  21. 21.

    Geng, R., Mansouri, S. A., & Aktas, E. (2017). The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies. International Journal of Production Economics, 183, 245–258.

    Google Scholar 

  22. 22.

    Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Fosso Wamba, S. (2017). World class sustainable supply chain management: Critical review and further research directions. The International Journal of Logistics Management, 28(2), 332–362.

    Google Scholar 

  23. 23.

    Krikke, H., Van Harten, A., & Schuur, P. (1999). Business case Oce: Reverse logistics network redesign for copiers. OR Spectrum, 21, 381–409.

    MATH  Google Scholar 

  24. 24.

    Autry, C. W., Daugherty, P. J., & Richey, R. G. (2001). The challenge of reverse logistics in catalog retailing. International Journal of Physical Distribution & Logistics, 31(1), 26–37.

    Google Scholar 

  25. 25.

    Peralta, G. L., & Fontanos, P. M. (2006). E-waste issues and measures in the Philippines. The Journal of Material Cycles and Waste Management, 8(1), 34–39.

    Google Scholar 

  26. 26.

    Srivastava, S. K., & Srivastava, R. K. (2006). Managing product returns for reverse logistics. International Journal of Physical Distribution & Logistics, 36(7), 524–546.

    Google Scholar 

  27. 27.

    Xiaofeng, X., & Tijun, F. (2009). Forecast for the amount of returned products based on wave function. International Conference on Information Management, Innovation Management and Industrial Engineering, 2, 324–327.

    Google Scholar 

  28. 28.

    Lau, K. H., & Wang, Y. (2009). Reverse logistics in the electronic industry of China: A case study. Supply Chain Management, 14(6), 447–465.

    Google Scholar 

  29. 29.

    Janes, B., Schuur, P., & De Brito, M. P. (2010). A reverse logistics diagnostic tool: the case of the consumer electronics industry. International Journal of Advanced Manufacturing Technolgy, 47(5–8), 495–513.

    Google Scholar 

  30. 30.

    Chiou, C. Y., Chen, H. C., Yu, C. T., & Yeh, C. Y. (2012). Consideration factors of reverse logistics implementation-A case study of Taiwan’s electronics industry. Procedia-Social and Behavioral Sciences, 40, 375–381.

    Google Scholar 

  31. 31.

    Potdar, A., & Rogers, J. (2012). Reason-code based model to forecast product returns. Foresight, 14(2), 105–120.

    Google Scholar 

  32. 32.

    Achillas, C., Vlachokostas, C., Aidonis, D., Moussiopoulos, Ν., Iakovou, E., & Banias, G. (2010). Optimising reverse logistics network to support policy-making in the case of electrical and electronic equipment. Waste Management, 30(12), 2592–2600.

    Google Scholar 

  33. 33.

    Kissling, R., Fitzpatrick, C., Boeni, H., Luepschen, C., Andrew, S., & Dickenson, J. (2012). Definition of generic re-use operating models for electrical and electronic equipment. Resources, Conservation and Recycling, 65, 85–99.

    Google Scholar 

  34. 34.

    Krapp, M., Nebel, J., & Sahamie, H. (2013). Using forecasts and managerial accounting information to enhance closed-loop supply chain management. OR Spectrum, 35(4), 975–1007.

    MathSciNet  MATH  Google Scholar 

  35. 35.

    Ravi, V., & Shankar, R. (2004). Analysis of interactions among the barriers of reverse logistics. Technological Forecasting and Social Change, 72(8), 1011–1029.

    Google Scholar 

  36. 36.

    González-Torre, P., Álvarez, M., Sarkis, J., & Adenso-Dııaz, B. (2010). Barriers to the implementation of environmentally oriented reverse logistics: Evidence from the automotive industry sector. British Journal of Management, 21(4), 889–904.

    Google Scholar 

  37. 37.

    Tognetti, A., Grosse-Ruyken, P. T., & Wagner, S. M. (2015). Green supply chain network optimization and the trade-off between environmental and economic objectives. International Journal of Production Economics, 170, 385–392.

    Google Scholar 

  38. 38.

    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, 75–79.

    Google Scholar 

  39. 39.

    Matar, N., Jaber, M. Y., & Searcy, C. (2014). A reverse logistics inventory model for plastic bottles. The International Journal of Logistics Management, 25(2), 315–333.

    Google Scholar 

  40. 40.

    Marmolejo, J. A., Rodríguez, R., Cruz-Mejia, O., & Saucedo, J. (2016). Design of a distribution network using primal-dual decomposition. Mathematical Problems in Engineering, 2016, 7851625.

    MathSciNet  Article  MATH  Google Scholar 

  41. 41.

    Paydar, M. M., & Olfati, M. (2018). Designing and solving a reverse logistics network for polyethylene terephthalate bottles. Journal of Cleaner Production, 195, 605–617.

    Google Scholar 

  42. 42.

    Rogers, D. S., & Tibben-Lembke, R. (1999). Going backwards: reverse logistics trends and practices. Reno, NV: Reverse Logistics Executive Council.

    Google Scholar 

  43. 43.

    Abraham, N. (2011). The apparel aftermarket in India—A case study focusing on reverse logistics. Journal of Fashion Marketing and Management, 15(2), 211–227.

    Google Scholar 

  44. 44.

    Guo, J., Wang, X., Fan, S., & Gen, M. (2017). Forward and reverse logistics network and route planning under the environment of low-carbon emissions: A case study of Shanghai fresh food E-commerce enterprises. Computers & Industrial Engineering, 106, 351–360.

    Google Scholar 

  45. 45.

    Louwers, D., Kip, B. J., Peters, E., Souren, F., & Flapper, S. D. P. (1999). A facility location allocation model for reusing carpet material. Computer Ind Engineering, 36(4), 855–869.

    Google Scholar 

  46. 46.

    Realff, M. J., Ammons, J. C., & Newton, D. J. (2004). Robust reverse production system design for carpet recycling. IIE Transactions, 36(8), 767–776.

    Google Scholar 

  47. 47.

    Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications: A state-of-the-art survey. Berlin, Heidelberg: Springer.

    MATH  Google Scholar 

  48. 48.

    Krikke, H. R., Bloemh of-Ruwaard, J., & Van Wassenhove, L. N. (2003). Concurrent product and closed-loop supply chain design with an application to refrigerators. International Journal of Production Research, 41(16), 3689–3719.

    MATH  Google Scholar 

  49. 49.

    Xie, X., Zeng, S., Peng, Y., & Tam, C. (2013). What affects the innovation performance of small and medium-sized enterprises in China? Innovation, 15(3), 271–286.

    Google Scholar 

  50. 50.

    Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research, 220(1), 67–78.

    MathSciNet  MATH  Google Scholar 

  51. 51.

    Liang, T. F., & Cheng, H. W. (2009). Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36(2), 3367–3377.

    Google Scholar 

  52. 52.

    Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Google Scholar 

  53. 53.

    Krikke, H. (2011). Impact of closed-loop network configurations on carbon footprints: a case study in copiers. Resource Conservation Recycling, 55(12), 1196–1205.

    Google Scholar 

  54. 54.

    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.

    Google Scholar 

  55. 55.

    Azadi, M., Shabani, A., Khodakarami, M., & Saen, R. F. (2014). Planning in feasible region by two-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers. Transportation Research Part E: Logistics and Transportation Review, 70, 324–338.

    Google Scholar 

  56. 56.

    Francesco, M. D., Crainic, T. G., & Zuddas, P. (2009). The effect of multi-scenario policies on empty container repositioning. Transportation Research Part E, 45(5), 758–770.

    Google Scholar 

  57. 57.

    Meng, Q., & Wang, S. (2011). Liner shipping service network design with empty container repositioning. Transportation Research Part E, 47(5), 695–708.

    Google Scholar 

  58. 58.

    Sasikumar, P., Kannan, G., & Haq, A. N. (2010). A multi-echelon reverse logistics network design for product recovery—A case of truck tire remanufacturing. International Journal of Advanced Manufacturing Technology, 49(9–12), 1223–1234.

    Google Scholar 

  59. 59.

    Guide, V. D. R., Jr., & Pentico, D. W. (2003). A hierarchical decision model for re-manufacturing and re-use. International Journal of Logistics: Research & Applications, 6(1–2), 29–35.

    Google Scholar 

  60. 60.

    Geyer, R., & Blass, V. D. (2010). The economics of cell phone reuse and recycling. International Journal of Advanced Manufacturing Technology, 47(5–8), 515–525.

    Google Scholar 

  61. 61.

    Kannan, G., Palaniappan, M., Zhu, Q., & Kannan, D. (2012). Analysis of third-party reverse logistics provider using interpretive structural modeling. International Journal of Production Research, 140(1), 204–211.

    Google Scholar 

  62. 62.

    Stock, James, & Mulki, Jay. (2009). Product returns processing: An examination of practices of manufacturers, wholesalers/distributors, and retailers. Journal of Business Logistics, 30, 33–62.

    Article  Google Scholar 

  63. 63.

    Fleischmann, M., Beullens, P., Dekker, R., Bloemhof-Ruwaard, J. M., & Wassenhove L. N. Van (2001). The impact of product recovery on logistics network design, 10, 156–173.

  64. 64.

    Rao, P., & Holt, D. (2005). Do green supply chains lead to competitiveness and economic performance? The International Journal of Operations & Production Management, 25, 898–916.

    Google Scholar 

  65. 65.

    Shang, K. C., Lu, C. S., & Li, S. (2010). A taxonomy of green supply chain management capability among electronics-related manufacturing firms in Taiwan. Journal of Environmental Management, 91, 1218–1226.

    Google Scholar 

  66. 66.

    Zhu, Q., Sarkis, J., & Lai, K. (2007). Green supply chain management: pressures, practices and performance within Chinese automobile industry. Journal of Cleaner Production, 15, 1041–1052.

    Google Scholar 

  67. 67.

    Kannan, D., Diabat, A., & Shankar, K. M. (2012). Analyzing the drivers of end-of-life tire management using interpretive structural modeling (ISM). International Journal of Advanced Manufacturing Technology, 72(9–12), 1603–1614.

    Google Scholar 

  68. 68.

    Mittal, V. K., & Sangwan, K. S. (2013). Assessment of hierarchy and inter-relationships of barriers to environmentally conscious manufacturing adoption. World Journal of Science, Technology and Sustainable Development, 10(4), 297–307.

    Google Scholar 

  69. 69.

    Kilic, H. S., Cebeci, U., & Ayhan, M. B. (2015). Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey. Resources, Conservation and Recycling, 95, 120–132.

    Google Scholar 

  70. 70.

    Varsei, M., & Polyakovskiy, S. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. Omega, 66, 236–247.

    Google Scholar 

  71. 71.

    Liao, T. Y. (2018). Reverse logistics network design for product recovery and remanufacturing. Applied Mathematical Modelling, 60, 145–163.

    MathSciNet  MATH  Google Scholar 

  72. 72.

    Kuşakcı, A. O., Ayvaz, B., Cin, E., & Aydın, N. (2019). Optimization of reverse logistics network of End of Life Vehicles under fuzzy supply: A case study for Istanbul Metropolitan Area. Journal of Cleaner Production, 215, 1036–1051.

    Google Scholar 

  73. 73.

    Kogg, B., & Mont, O. (2012). Environmental and social responsibility in supply chains: The practise of choice and inter-organisational management. Ecological Economics, 83, 154–163.

    Google Scholar 

  74. 74.

    Mohanty, R. P., & Prakash, A. (2014). Green supply chain management practices in India: An empirical study. Production Planning & Control, 25(16), 1322–1337.

    Google Scholar 

  75. 75.

    Perotti, S., Micheli, G. J., & Cagno, E. (2015). Motivations and barriers to the adoption of green supply chain practices among 3PLs. International Journal of Logistics Systems and Management, 20(2), 179–198.

    Google Scholar 

  76. 76.

    Abdessalem, M., Hadj-Alouane, A. B., & Riopel, D. (2012). Decision modelling of reverse logistics systems: selection of recovery operations for end-of-life products. Interantional Journal of Logistics Systems and Management, 13(2), 139.

    Google Scholar 

  77. 77.

    Ali, S. S., Madaan, J., Chan, F. T. S., & Kannan, S. (2013). Inventory management of perishable products: A time decay linked logistic approach. International Journal of Production Research, 51(13), 3864–3879.

    Google Scholar 

  78. 78.

    Kumar, S., & Putnam, V. (2008). Cradle to cradle: Reverse logistics strategies and opportunities across three industry sectors. International Journal of Production Economic, 115, 305–315.

    Article  Google Scholar 

  79. 79.

    Thierry, M., Salomon, M., Nunen, J., & Wassenhove, L. (1995). Strategic issues in product recovery management. California Management Review, 37(2), 114–135.

    Google Scholar 

  80. 80.

    Tibben-Lembke, R. S., & Rogers, D. S. (2002). Differences between forward and reverse logistics in a retail environment. Supply Chain Management: An International Journal, 7, 271–282.

    Google Scholar 

  81. 81.

    De Brito, M., & Dekker, R. (2002). Reverse logistics—A framework. Rotterdam: Erasmus University Rotterdam, Econometric Institute, Econometric Institute Report.

    Google Scholar 

  82. 82.

    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.

    Google Scholar 

  83. 83.

    Jayaraman, V., & Luo, Y. (2007). Creating competitive advantages through new value creation: A reverse logistics perspective. Academy of Management Perspectives, 21(2), 56–73.

    Google Scholar 

  84. 84.

    Rubio, S., Chamorro, A., & Miranda, F. J. (2008). Characteristics of the research on reverse logistics (1995–2005). International Journal of Production Research, 46, 1099–1120.

    MATH  Google Scholar 

  85. 85.

    Cruz-Rivera, R., & Ertel, J. (2009). Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico. European Journal of Operational Research, 196, 930–939.

    Article  MATH  Google Scholar 

  86. 86.

    Pourjavad, E., & Mayorga, R. V. (2018). Optimization of a sustainable closed loop supply chain network design under uncertainty using multi-objective evolutionary algorithms. Advances in Production Engineering & Management., 13, 216–228.

    Article  Google Scholar 

  87. 87.

    Epel, E. S., Blackburn, E. H., Lin, J., Dhabhar, F. S., Adler, N. E., Morrow, J. D., et al. (2004). Accelerated telomere shortening in response to life stress. Proceedings of the National Academy of Sciences, 101(49), 17312–17315.

    Google Scholar 

  88. 88.

    Fleischmann, M., Krikke, H. R., Dekker, R., & Flapper, S. P. D. (2000). A characterization of logistics networks for product recovery. Omega, 28, 653–666.

    Google Scholar 

  89. 89.

    Jayaraman, V., Guide, V. D. R., & Srivastava, R. (1999). A closed-loop logistics model for remanufacturing. Journal of Operational Research Society, 50(5), 497–508.

    MATH  Google Scholar 

  90. 90.

    Vahdani, B., Jolai, F., Tavakkoli-Moghaddam, R., & Mousavi, S. M. (2012). Two fuzzy possibilistic bi-objective zero one programming models for outsourcing the equipment maintenance problem. Engineering Optimization, 44(7), 801–820.

    MathSciNet  Google Scholar 

  91. 91.

    Schrijver, A. (2003). Combinatorial optimization: Polyhedra and efficiency. Berlin: Springer.

    MATH  Google Scholar 

  92. 92.

    Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990–4012.

    MathSciNet  MATH  Google Scholar 

  93. 93.

    Zeballos, L. J., Gomes, M. I., Barbosa-Povoa, A. P., & Novais, A. Q. (2012). Addressing the uncertain quality and quantity of returns in closed-loop supply chains. Computers & Chemical Engineering, 47, 237–247.

    Google Scholar 

  94. 94.

    Hasani, A., Zegordi, S. H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50(16), 4649–4669.

    Google Scholar 

  95. 95.

    Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), 1931–1938.

    Google Scholar 

  96. 96.

    Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51, 262–269.

    Article  Google Scholar 

  97. 97.

    Jamshidi, R., Fatemi Ghomi, S., & Karimi, B. (2012). Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Scientia Iranica, 19(6), 1876–1886.

    Google Scholar 

  98. 98.

    Hu, T. L., Sheu, J. B., & Huang, K. H. (2002). A reverse logistics cost minimization model for the treatment of hazardous wastes. Transportation Research Part E: Logistics and Transportation Review, 38(6), 457–473.

    Google Scholar 

  99. 99.

    Kim, K., Song, I., Kim, J., & Jeong, B. (2006). Supply planning model for remanufacturing system in reverse logistics environment. Computers & Industrial Engineering, 51(2), 279–287.

    Article  Google Scholar 

  100. 100.

    Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistic.

  101. 101.

    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.

    Google Scholar 

  102. 102.

    Lee, D. H., & Dong, M. (2009). Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 45(1), 61–71.

    Google Scholar 

  103. 103.

    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.

    Google Scholar 

  104. 104.

    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.

    MathSciNet  MATH  Google Scholar 

  105. 105.

    Niknejad, A., & Petrovic, D. (2014). Optimisation of integrated reverse logistics networks with different product recovery routes. European Journal of Operational Research, 238(1), 143–154.

    MathSciNet  MATH  Google Scholar 

  106. 106.

    Manuel Monsreal Barrera, M., & Cruz-Mejia, O. (2014). Reverse logistics of recovery and recycling of non-returnable beverage containers in the brewery industry: A “profitable visit” algorithm. International Journal of Physical Distribution & Logistics Management, 44(7), 577–596.

    Google Scholar 

  107. 107.

    Choudhary, A., Sarkar, S., Settur, S., & Tiwari, M. K. (2015). A carbon market sensitive optimization model for integrated forward–reverse logistics. International Journal of Production Economics, 164, 433–444.

    Google Scholar 

  108. 108.

    Govindan, K., Paam, P., & Abtahi, A. R. (2016). A fuzzy multi-objective optimization model for sustainable reverse logistics network design. Ecological Indicators, 67, 753–768.

    Google Scholar 

  109. 109.

    Arampantzi, C., & Minis, I. (2017). A new model for designing sustainable supply chain networks and its application to a global manufacturer. Journal of Cleaner Production, 156, 276–292.

    Google Scholar 

  110. 110.

    John, S. T., Sridharan, R., Kumar, P. R., & Krishnamoorthy, M. (2018). Multi-period reverse logistics network design for used refrigerators. Applied Mathematical Modelling, 54, 311–331.

    MathSciNet  MATH  Google Scholar 

  111. 111.

    Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Amin, S. H. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236–250.

    Google Scholar 

  112. 112.

    Bal, A., & Satoglu, S. I. (2018). A goal programming model for sustainable reverse logistics operations planning and an application. Journal of Cleaner Production, 201, 1081–1091.

    Google Scholar 

  113. 113.

    Kim, J., Do Chung, B., 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.

    Google Scholar 

  114. 114.

    Tsao, Y. C., Thanh, V. V., Lu, J. C., & Yu, V. (2018). Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming. Journal of Cleaner Production, 174, 1550–1565.

    Article  Google Scholar 

  115. 115.

    Govindan, K., Jafarian, A., & Nourbakhsh, V. (2018). Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics. Computers & Operations Research.

  116. 116.

    Wang, X., Zhao, M., & He, H. (2018). Reverse logistic network optimization research for sharing bikes. Procedia Computer Science, 126, 1693–1703.

    Google Scholar 

  117. 117.

    Sahebjamnia, N., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of Cleaner Production, 196, 273–296.

    Google Scholar 

  118. 118.

    Qiu, Y., Ni, M., Wang, L., Li, Q., Fang, X., & Pardalos, P. M. (2018). Production routing problems with reverse logistics and remanufacturing. Transportation Research Part E: Logistics and Transportation Review, 111, 87–100.

    Google Scholar 

  119. 119.

    Jin, H., Song, B. D., Yih, Y., & Sutherland, J. W. (2019). A bi-objective network design for value recovery of neodymium-iron-boron magnets: A case study of the United States. Journal of Cleaner Production, 211, 257–269.

    Google Scholar 

  120. 120.

    Zarbakhshnia, N., Soleimani, H., Goh, M., & Razavi, S. S. (2019). A novel multi-objective model for green forward and reverse logistics network design. Journal of Cleaner Production, 208, 1304–1316.

    Article  Google Scholar 

  121. 121.

    Rahimi, M., Ghezavati, V., & Asadi, F. (2019). A stochastic risk-averse sustainable supply chain network design problem with quantity discount considering multiple sources of uncertainty. Computers & Industrial Engineering.

  122. 122.

    Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and System, 1, 44–55.

    MathSciNet  MATH  Google Scholar 

  123. 123.

    Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Journal of Management Science, 17(4), 141–164.

    MathSciNet  MATH  Google Scholar 

  124. 124.

    Behera, S. K., & Nayak, J. R. (2011). Solution of multi-objective mathematical programming problems in fuzzy approach. International Journal on Computer Science and Engineering, 3(12), 3790.

    Google Scholar 

  125. 125.

    Tiwari, R. N., Dharmahr, S., & Rao, J. R. (1987). Fuzzy goal programming-an additive model. Fuzzy Sets and Systems, 24(1), 27–34.

    MathSciNet  MATH  Google Scholar 

  126. 126.

    Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

    Google Scholar 

  127. 127.

    Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42(23), 9152–9164.

    Google Scholar 

  128. 128.

    Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130.

    Google Scholar 

  129. 129.

    Kumar, D., Rahman, Z., & Chan, F. (2017). A Fuzzy AHP and Fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study. International Journal of Computer Integrated Manufacturing, 30, 535–551.

    Article  Google Scholar 

  130. 130.

    Kilic, E., Ali, S. S., Weber, G. W., & Dubey, R. (2014). A value-adding approach to reliability under preventive maintenance costs and its applications. Optimization, 63(12), 1805–1816.

    MathSciNet  MATH  Google Scholar 

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We wish to express our gratitude to organizations for their unconditional and continuous support in providing data for our research. We are also indebted to the members of managerial team who provided us with their critical remarks, insights and expertise thus navigating us in the right direction. We would also like to extend our gratitude to our esteemed reviewers for their feedback which helped us to improve the quality of our manuscript. This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. G-1340-144-1440. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

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Ali, S.S., Paksoy, T., Torğul, B. et al. Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach. Wireless Netw 26, 5759–5782 (2020).

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  • BWM
  • Fuzzy multi-objective linear programming
  • Mixed-integer linear programming
  • Network design
  • Product recovery
  • Reverse logistics