Annals of Operations Research

, Volume 273, Issue 1–2, pp 693–738 | Cite as

Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem

  • Jyoti Dhingra Darbari
  • Devika KannanEmail author
  • Vernika Agarwal
  • P. C. Jha
S.I.: OR in Transportation


Immense concern for sustainability and increasing stakeholders’ involvement has sparked tremendous interest towards designing optimal supply chain networks with significant economic, environmental, and social influence. Central to the idea, this study aims to design a closed loop supply chain (CLSC) network for an Indian laptop manufacturer. The network configuration, which involves a manufacturer, suppliers, third party logistics providers (forward and reverse), retailers, customers and a non-government organisation (NGO), is modelled as a mixed integer linear programming problem with fuzzy goals of minimising environmental impact and maximising net profit and social impact, subject to fuzzy demand and capacity constraints. Profit is generated from the sale of primary and secondary laptops, earned tax credits, and revenue sharing with reverse logistics providers. The environmental implications are investigated by measuring the carbon emitted due to activities of manufacturing, assembling, dismantling, fabrication, and transportation. The social dimension is quantified in terms of the number of jobs created, training hours, community service hours, and donations to NGO. The novelty of the model rests on its quantification of the three triple bottom line (TBL) indicators and on its use of AHP–TOPSIS for modelling the multi-criteria perspectives of the stakeholders. Numerical weights for the triple lines of sustainability are utilized. Further, a fuzzy multi-objective programming approach that integrates fuzzy set theory with goal programming techniques is utilised to yield properly efficient solutions to the multi-objective problem and to provide a trade-off set for conflicting objectives. The significance of the CLSC model is empirically established as a decision support tool for improving the TBL performance of a particular Indian laptop manufacturer. Practical and theoretical implications are derived from the result analysis, and a generalised quantitative closed-loop model can be effectively adapted by other electronic manufacturers to increase their competitiveness, profitability, and to improve their TBL.


Closed loop supply chain AHP–TOPSIS Triple bottom line Multi-objective Fuzzy goal programming 


  1. Accorsi, R., Manzini, R., Pini, C., & Penazzi, S. (2015). On the design of closed-loop networks for product life cycle management: Economic, environmental and geography considerations. Journal of Transport Geography, 48, 121–134.Google Scholar
  2. Altmann, M. (2015). A supply chain design approach considering environmentally sensitive customers: The case of a German manufacturing SME. International Journal of Production Research, 53(21), 6534–6550.Google Scholar
  3. 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.Google Scholar
  4. Battini, D., Persona, A., & Sgarbossa, F. (2014). A sustainable EOQ model: Theoretical formulation and applications. International Journal of Production Economics, 149, 145–153.Google Scholar
  5. Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B-141.Google Scholar
  6. Boukherroub, T., Ruiz, A., Guinet, A., & Fondrevelle, J. (2015). An integrated approach for sustainable supply chain planning. Computers & Operations Research, 54, 180–194.Google Scholar
  7. Brandenburg, M., & Rebs, T. (2015). Sustainable supply chain management: A modeling perspective. Annals of Operations Research, 229(1), 213–252.Google Scholar
  8. 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.Google Scholar
  9. Cassen, R. H. (1987). Our common future: Report of the World Commission on Environment and Development. International Affairs, 64(1), 126–126.Google Scholar
  10. Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135(1), 37–49.Google Scholar
  11. Chen, Z., & Andresen, S. (2014). A multiobjective optimization model of production-sourcing for sustainable supply chain with consideration of social, environmental, and economic factors. Mathematical Problems in Engineering, 2014, 616107.
  12. Chuang, C. H., Wang, C. X., & Zhao, Y. (2014). Closed-loop supply chain models for a high-tech product under alternative reverse channel and collection cost structures. International Journal of Production Economics, 156, 108–123.Google Scholar
  13. CPCB (2014). List of registered E-waste dismantler/recycler in the country. Accessed 24 Nov 2016.
  14. Cruz, J. M. (2013). Modeling the relationship of globalized supply chains and corporate social responsibility. Journal of Cleaner Production, 56, 73–85.Google Scholar
  15. Daghigh, R., Jabalameli, M., Amiri, A., & Pishvaee, M. (2016). A multi-objective location-inventory model for 3PL providers with sustainable considerations under uncertainty. International Journal of Industrial Engineering Computations, 7(4), 615–634.Google Scholar
  16. Darbari, J. D., Agarwal, V., Chaudhary, K., & Jha, P. C. (2015). Multi-criteria decision approach for a sustainable reverse logistics network under fuzzy environment. In International Conference on Industrial Engineering and Operations Management (IEOM) 2015, (pp. 1–7). IEEE.Google Scholar
  17. De Giovanni, P. (2014). Environmental collaboration in a closed-loop supply chain with a reverse revenue sharing contract. Annals of Operations Research, 220(1), 135–157.Google Scholar
  18. 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.Google Scholar
  19. Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 235(3), 594–615.Google Scholar
  20. Diabat, A., Abdallah, T., Al-Refaie, A., Svetinovic, D., & Govindan, K. (2013). Strategic closed-loop facility location problem with carbon market trading. IEEE Transactions on engineering Management, 60(2), 398–408.Google Scholar
  21. Dwivedy, M., & Mittal, R. K. (2010). Future trends in computer waste generation in India. Waste management, 30(11), 2265–2277.Google Scholar
  22. Elhedhli, S., & Merrick, R. (2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D: Transport and Environment, 17(5), 370–379.Google Scholar
  23. Elkington, J. (1998). Partnerships from cannibals with forks: The triple bottom line of 21st-century business. Environmental Quality Management, 8(1), 37–51.Google Scholar
  24. Ernst, D. (2014). Upgrading India’s electronics manufacturing industry: Regulatory reform and industrial policy, a special stusy. Honolulu, Hawaii: East-West Centre.Google Scholar
  25. Erol, I., Sencer, S., & Sari, R. (2011). A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain. Ecological Economics, 70(6), 1088–1100.Google Scholar
  26. Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, 54, 11–32.Google Scholar
  27. Fahimnia, B., Sarkis, J., Dehghanian, F., Banihashemi, N., & Rahman, S. (2013). The impact of carbon pricing on a closed-loop supply chain: An Australian case study. Journal of Cleaner Production, 59, 210–225.Google Scholar
  28. Fattahi, M., & Govindan, K. (2017). Integrated forward/reverse logistics network design under uncertainty with pricing for collection of used products. Annals of Operations Research, 253(1), 193–225.Google Scholar
  29. Gaur, J., Subramoniam, R., Govindan, K., & Huisingh, D. (2016). Closed-loop supply chain management: From conceptual to an action oriented framework on core acquisition. Journal of Cleaner Production, 30, 1e10.Google Scholar
  30. Gimenez, C., Sierra, V., & Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line. International Journal of Production Economics, 140(1), 149–159.Google Scholar
  31. Govindan, K., Jafarian, A., & Nourbakhsh, V. (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Computers & Operations Research, 62, 112–130.Google Scholar
  32. Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9–28.Google Scholar
  33. Govindan, K., Jha, P. C., & Garg, K. (2016a). Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing. International Journal of Production Research, 54(5), 1463–1486.Google Scholar
  34. Govindan, K., Khodaverdi, R., & Jafarian, A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 47, 345–354.Google Scholar
  35. 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.Google Scholar
  36. Govindan, K. (2017). Sustainable consumption and production in the food supply chain: A conceptual framework. International Journal of Production Economics.
  37. Govindan, K., Paam, P., & Abtahi, A. R. (2016b). A fuzzy multi-objective optimization model for sustainable reverse logistics network design. Ecological Indicators, 67, 753–768.Google Scholar
  38. Govindan, K., 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.Google Scholar
  39. Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains: A journal of cleaner production focus. Journal of Cleaner Production, 142, 371–384.Google Scholar
  40. Guide, V. D. R, Jr., & Van Wassenhove, L. N. (2009). OR FORUM—The evolution of closed-loop supply chain research. Operations Research, 57(1), 10–18.Google Scholar
  41. Hassini, E., Surti, C., & Searcy, C. (2012). A literature review and a case study of sustainable supply chains with a focus on metrics. International Journal of Production Economics, 140(1), 69–82.Google Scholar
  42. Hollos, D., Blome, C., & Foerstl, K. (2012). Does sustainable supplier co-operation affect performance? Examining implications for the triple bottom line. International Journal of Production Research, 50(11), 2968–2986.Google Scholar
  43. 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.Google Scholar
  44. Jaehn, F. (2016). Sustainable operations. European Journal of Operational Research, 253(2), 243–264.Google Scholar
  45. Jindal, A., & Sangwan, K. S. (2017). Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors. Annals of Operations Research, 257(1–2), 95–120.Google Scholar
  46. Kannan, D., Govindan, K., & Shankar, M. (2016). India: Formalize recycling of electronic waste. Nature, 530(7590), 281–281.Google Scholar
  47. Kleindorfer, P. R., Singhal, K., & Wassenhove, L. N. (2005). Sustainable operations management. Production and Operations Management, 14(4), 482–492.Google Scholar
  48. Kuehr, R. (2003). Managing PCS through policy: Review and ways to extend lifespan. In R. Kuehr & E. Williams (Eds.), Computers and the environment: Understanding and managing their impacts (pp. 253–278). Netherlands: Springer.Google Scholar
  49. Kumar, R. S., Choudhary, A., Babu, S. A. I., Kumar, S. K., Goswami, A., & Tiwari, M. K. (2017). Designing multi-period supply chain network considering risk and emission: A multi-objective approach. Annals of Operations Research, 250(2), 427–461.Google Scholar
  50. Kumar, S., Teichman, S., & Timpernagel, T. (2012). A green supply chain is a requirement for profitability. International Journal of Production Research, 50(5), 1278–1296.Google Scholar
  51. Liang, T. F. (2008). Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Computers & Industrial Engineering, 55(3), 676–694.Google Scholar
  52. 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
  53. Mangla, S. K., Govindan, K., & Luthra, S. (2017). Prioritizing the barriers to achieve sustainable consumption and production trends in supply chains using fuzzy analytical hierarchy process. Journal of Cleaner Production, 151, 509–525.Google Scholar
  54. 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
  55. Millet, D. (2011). Designing a sustainable reverse logistics channel: The 18 generic structures framework. Journal of Cleaner Production, 19(6), 588–597.Google Scholar
  56. Min, H., & Kim, I. (2012). Green supply chain research: Past, present, and future. Logistics Research, 4(1–2), 39–47.Google Scholar
  57. Mishima, K., & Mishima, N. (2011). A study on determination of upgradability of laptop PC components. In J. Hesselbach & C. Herrmann (Eds.), Functional thinking for value creation (pp. 297–302). Berlin: Springer.Google Scholar
  58. Mohamed, R. H. (1997). The relationship between goal programming and fuzzy programming. Fuzzy Sets and Systems, 89(2), 215–222.Google Scholar
  59. Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2015). Towards supply chain sustainability: economic, environmental and social design and planning. Journal of Cleaner Production, 105, 14–27.Google Scholar
  60. Mousazadeh, M., Torabi, S. A., & Pishvaee, M. S. (2014). Green and reverse logistics management under fuzziness. In C. Kahraman & B. Öztayşi (Eds.), Supply chain management under fuzziness (pp. 607–637). Berlin: Springer.Google Scholar
  61. Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision Sciences, 11(2), 325–336.Google Scholar
  62. 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, 173–184.Google Scholar
  63. Önüt, S., & Soner, S. (2008). Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. Waste Management, 28(9), 1552–1559.Google Scholar
  64. Özkır, V., & Başlıgil, H. (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment. Journal of Cleaner Production, 41, 114–125.Google Scholar
  65. Paksoy, T., Bektaş, T., & Özceylan, E. (2011). Operational and environmental performance measures in a multi-product closed-loop supply chain. Transportation Research Part E: Logistics and Transportation Review, 47(4), 532–546.Google Scholar
  66. Perçin, S. (2009). Evaluation of third-party logistics (3PL) providers by using a two-phase AHP and TOPSIS methodology. Benchmarking: An International Journal, 16(5), 588–604.Google Scholar
  67. Pérez-Fortes, M., Laínez-Aguirre, J. M., Arranz-Piera, P., Velo, E., & Puigjaner, L. (2012). Design of regional and sustainable bio-based networks for electricity generation using a multi-objective MILP approach. Energy, 44(1), 79–95.Google Scholar
  68. Pinto-Varela, T., Barbosa-Póvoa, A. P. F., & Novais, A. Q. (2011). Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances. Computers & Chemical Engineering, 35(8), 1454–1468.Google Scholar
  69. Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433–3446.Google Scholar
  70. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20.Google Scholar
  71. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2014). An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review, 67, 14–38.Google Scholar
  72. Qin, X. S., Huang, G. H., Chakma, A., Nie, X. H., & Lin, Q. G. (2008). A MCDM-based expert system for climate-change impact assessment and adaptation planning—A case study for the Georgia Basin Canada. Expert Systems with Applications, 34(3), 2164–2179.Google Scholar
  73. Rajeev, A., Pati, R. K., Padhi, S. S., & Govindan, K. (2017). Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, 162, 299–314.Google Scholar
  74. 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
  75. Rathore, P., Kota, S., & Chakrabarti, A. (2011). Sustainability through remanufacturing in India: A case study on mobile handsets. Journal of Cleaner Production, 19(15), 1709–1722.Google Scholar
  76. Rubin, P. A., & Narasimhan, R. (1984). Fuzzy goal programming with nested priorities. Fuzzy Sets and Systems, 14(2), 115–129.Google Scholar
  77. Rubio, S., Chamorro, A., & Miranda, F. J. (2008). Characteristics of the research on reverse logistics (1995–2005). International Journal of Production Research, 46(4), 1099–1120.Google Scholar
  78. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.Google Scholar
  79. Santibañez-Aguilar, J. E., González-Campos, J. B., Ponce-Ortega, J. M., Serna-González, M., & El-Halwagi, M. M. (2014). Optimal planning and site selection for distributed multiproduct biorefineries involving economic, environmental and social objectives. Journal of Cleaner Production, 65, 270–294.Google Scholar
  80. Sarkis, J., Helms, M. M., & Hervani, A. A. (2010). Reverse logistics and social sustainability. Corporate Social Responsibility and Environmental Management, 17(6), 337–354.Google Scholar
  81. Selim, H., & Ozkarahan, I. (2008). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3–4), 401–418.Google Scholar
  82. Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54(4), 1513–1520.Google Scholar
  83. Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699–1710.Google Scholar
  84. Shi, J., Liu, Z., Tang, L., & Xiong, J. (2017). Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm. Applied Mathematical Modelling, 45, 14–30.Google Scholar
  85. Shokohyar, S., & Mansour, S. (2013). Simulation-based optimisation of a sustainable recovery network for Waste from Electrical and Electronic Equipment (WEEE). International Journal of Computer Integrated Manufacturing, 26(6), 487–503.Google Scholar
  86. Soysal, M., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. J. (2014). Modelling food logistics networks with emission considerations: The case of an international beef supply chain. International Journal of Production Economics, 152, 57–70.Google Scholar
  87. Sundarakani, B., De Souza, R., Goh, M., Wagner, S. M., & Manikandan, S. (2010). Modeling carbon footprints across the supply chain. International Journal of Production Economics, 128(1), 43–50.Google Scholar
  88. Talaei, M., Moghaddam, B. F., 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.Google Scholar
  89. Tang, C. S., & Zhou, S. (2012). Research advances in environmentally and socially sustainable operations. European Journal of Operational Research, 223(3), 585–594.Google Scholar
  90. Tiwari, R. N., Dharmar, S., & Rao, J. R. (1987). Fuzzy goal programming—An additive model. Fuzzy Sets and Systems, 24(1), 27–34.Google Scholar
  91. Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56–66.Google Scholar
  92. Wang, J. J., Jing, Y. Y., Zhang, C. F., & Zhao, J. H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13(9), 2263–2278.Google Scholar
  93. Wath, S. B., Dutt, P. S., & Chakrabarti, T. (2011). E-waste scenario in India, its management and implications. Environmental Monitoring and Assessment, 172(1), 249–262.Google Scholar
  94. Wath, S. B., Vaidya, A. N., Dutt, P. S., & Chakrabarti, T. (2010). A roadmap for development of sustainable E-waste management system in India. Science of the Total Environment, 409(1), 19–32.Google Scholar
  95. Wei, J., & Zhao, J. (2013). Reverse channel decisions for a fuzzy closed-loop supply chain. Applied Mathematical Modelling, 37(3), 1502–1513.Google Scholar
  96. Wilson, D. C., Velis, C., & Cheeseman, C. (2006). Role of informal sector recycling in waste management in developing countries. Habitat International, 30(4), 797–808.Google Scholar
  97. Winkler, H. (2011). Closed-loop production systems—A sustainable supply chain approach. CIRP Journal of Manufacturing Science and Technology, 4(3), 243–246.Google Scholar
  98. Zavadskas, Edmundas Kazimieras, & Turskis, Zenonas. (2011). Multiple criteria decision making (MCDM) methods in economics: An overview. Technological and Economic Development of Economy, 17(2), 397–427.Google Scholar
  99. Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location–routing–inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182–214.Google Scholar
  100. Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45–55.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Jyoti Dhingra Darbari
    • 1
  • Devika Kannan
    • 2
    Email author
  • Vernika Agarwal
    • 1
  • P. C. Jha
    • 1
  1. 1.Department of Operational ResearchUniversity of DelhiDelhiIndia
  2. 2.Center for Sustainable Supply Chain Engineering, Department of Technology and InnovationUniversity of Southern DenmarkOdense MDenmark

Personalised recommendations