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A Multi-Stage Stochastic Programming Model for the Multi-Echelon Multi-Period Reverse Logistics Problem. (2021). Azizi, Vahid ; Hu, Guiping.
In: Sustainability.
RePEc:gam:jsusta:v:13:y:2021:i:24:p:13596-:d:698343.

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  1. Model for Reverse Logistic Problem of Recycling under Stochastic Demand. (2022). Calipinar, Hatice ; Koc, Erdinc ; Desticioglu, Beste ; Ozyoruk, Bahar.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:8:p:4640-:d:792926.

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  2. Alshamsi, A.; Diabat, A. A Genetic Algorithm for Reverse Logistics network design: A case study from the GCC. J. Clean. Prod. 2017, 151, 652–669. [CrossRef]
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  5. Balde, C.P.; Wang, F.; Kuehr, R.; Huisman, J. The Global e-Waste Monitor 2014: Quantities, Flows and Resources; United Nations University, International Telecommunication Union, and International Solid Waste Association: Bonn, Germany, 2015.
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  6. Demirel, E.; Demirel, N.; Gökçen, H. A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. J. Clean. Prod. 2016, 112, 2101–2113. [CrossRef]
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  7. Govindan, K.; Fattahi, M.; Keyvanshokooh, E. Supply chain network design under uncertainty: A comprehensive review and future research directions. Eur. J. Oper. Res. 2017, 263, 108–141. [CrossRef]
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  8. John, S.T.; Sridharan, R.; Kumar, P.R.; Krishnamoorthy, M. Multi-period reverse logistics network design for used refrigerators. Appl. Math. Model. 2018, 54, 311–331. [CrossRef]
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  10. Lee, D.H.; Dong, M. Dynamic network design for reverse logistics operations under uncertainty. Transp. Res. Part E Logist. Transp. Rev. 2009, 45, 61–71. [CrossRef]

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  13. Min, H.; Ko, H.J.; Ko, C.S. A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega 2006, 34, 56–69. [CrossRef]

  14. Moktadir, M.A.; Rahman, T.; Ali, S.M.; Nahar, N.; Paul, S.K. Examining barriers to reverse logistics practices in the leather footwear industry. Ann. Oper. Res. 2020, 93, 715–746. [CrossRef]

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  16. Niknejad, A.; Petrovic, D. Optimisation of integrated reverse logistics networks with different product recovery routes. Eur. J. Oper. Res. 2014, 238, 143–154. [CrossRef]

  17. Prajapati, H.; Kant, R.; Shankar, R. Bequeath life to death: State-of-art review on reverse logistics. J. Clean. Prod. 2019, 211, 503–520. [CrossRef]
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  18. Rachih, H.; Mhada, F.Z.; Chiheb, R. Meta-heuristics for reverse logistics: A literature review and perspectives. Comput. Ind. Eng. 2019, 127, 45–62. [CrossRef]
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  19. Rahimi, M.; 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. J. Clean. Prod. 2018, 172, 1567–1581. [CrossRef]
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  20. Salema, M.I.G.; Barbosa-Povoa, A.P.; Novais, A.Q. An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. Eur. J. Oper. Res. 2007, 179, 1063–1077. [CrossRef]

  21. Silva, D.A.L.; Reno, G.W.S.; Sevegnani, G.; Sevegnani, T.B.; Truzzi, O.M.S. Comparison of disposable and returnable packaging: A case study of reverse logistics in Brazil. J. Clean. Prod. 2013, 47, 377–387. [CrossRef]
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  23. Trochu, J.; Chaabane, A.; Ouhimmou, M. A carbon-constrained stochastic model for eco-efficient reverse logistics network design under environmental regulations in the CRD industry. J. Clean. Prod. 2020, 245, 118818. [CrossRef]
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  24. Yu, H.; Solvang, W.D. A stochastic programming approach with improved multi-criteria scenario-based solution method for sustainable reverse logistics design of waste electrical and electronic equipment (WEEE). Sustainability 2016, 8, 1331. [CrossRef]

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