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A scenario-based robust possibilistic model for a multi-objective electronic reverse logistics network

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  • Tosarkani, Babak Mohamadpour
  • Amin, Saman Hassanzadeh
  • Zolfagharinia, Hossein

Abstract

Electronic reverse logistics topic has received growing attention because of its environmental and economic impact. In Canada, the province of Ontario has enacted regulations regarding the Waste Electrical and Electronic Equipment (WEEE) Recycling program. The objective of this study is to develop a novel scenario-based robust possibilistic approach to optimize and configure an electronic reverse logistics network by considering the uncertainty associated with fixed and variable costs, the quantity of demand and return, and the quality of returned products. A Monte Carlo simulation is utilized to analyze the performance of our proposed model. Then, ANOVA test is conducted to statistically verify our model using the simulation results. The mathematical model is extended to the multi-objective optimization by maximising the environmental compliance of the third parties. The efficient solutions of the multi-objective model are computed using the two-phase fuzzy compromise approach. To provide a comprehensive assessment of the problem under investigation, we provide sensitivity analyses on the impact of different factors (e.g., recovery rates, capacity of facilities) on the total expected profit. Several interesting results were obtained, including the fact that increasing the capacity of facilities does not automatically translate into higher profits. Furthermore, by comparing the efficient solutions of deterministic and robust modes, we illustrate the impact of robustness price on the multi-objective model. The application of the proposed model is illustrated using a network in the Greater Toronto Area (GTA) in Canada.

Suggested Citation

  • Tosarkani, Babak Mohamadpour & Amin, Saman Hassanzadeh & Zolfagharinia, Hossein, 2020. "A scenario-based robust possibilistic model for a multi-objective electronic reverse logistics network," International Journal of Production Economics, Elsevier, vol. 224(C).
  • Handle: RePEc:eee:proeco:v:224:y:2020:i:c:s0925527319303913
    DOI: 10.1016/j.ijpe.2019.107557
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    as
    1. Krumwiede, Dennis W. & Sheu, Chwen, 2002. "A model for reverse logistics entry by third-party providers," Omega, Elsevier, vol. 30(5), pages 325-333, October.
    2. Arenas Parra, M. & Bilbao Terol, A. & Perez Gladish, B. & Rodriguez Uria, M. V., 2005. "Solving a multiobjective possibilistic problem through compromise programming," European Journal of Operational Research, Elsevier, vol. 164(3), pages 748-759, August.
    3. Ramos, Tânia Rodrigues Pereira & Gomes, Maria Isabel & Barbosa-Póvoa, Ana Paula, 2014. "Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns," Omega, Elsevier, vol. 48(C), pages 60-74.
    4. Bohle, Carlos & Maturana, Sergio & Vera, Jorge, 2010. "A robust optimization approach to wine grape harvesting scheduling," European Journal of Operational Research, Elsevier, vol. 200(1), pages 245-252, January.
    5. Listes, Ovidiu & Dekker, Rommert, 2005. "A stochastic approach to a case study for product recovery network design," European Journal of Operational Research, Elsevier, vol. 160(1), pages 268-287, January.
    6. Nickel, Stefan & Saldanha-da-Gama, Francisco & Ziegler, Hans-Peter, 2012. "A multi-stage stochastic supply network design problem with financial decisions and risk management," Omega, Elsevier, vol. 40(5), pages 511-524.
    7. 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, Elsevier, vol. 89(C), pages 182-214.
    8. Zolfagharinia, Hossein & Hafezi, Maryam & Farahani, Reza Zanjirani & Fahimnia, Behnam, 2014. "A hybrid two-stock inventory control model for a reverse supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 141-161.
    9. Al-Othman, Wafa B.E. & Lababidi, Haitham M.S. & Alatiqi, Imad M. & Al-Shayji, Khawla, 2008. "Supply chain optimization of petroleum organization under uncertainty in market demands and prices," European Journal of Operational Research, Elsevier, vol. 189(3), pages 822-840, September.
    10. Reza Babazadeh & Fariborz Jolai & Jafar Razmi, 2015. "Developing scenario-based robust optimisation approaches for the reverse logistics network design problem under uncertain environments," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 20(4), pages 418-440.
    11. Kannan, G. & Sasi Kumar, P., 2009. "Developing the reverse logistics network--A comment and suggestions on minimizing the reverse logistics cost," Omega, Elsevier, vol. 37(3), pages 741-741, June.
    12. Kannan, Devika & Jabbour, Ana Beatriz Lopes de Sousa & Jabbour, Charbel José Chiappetta, 2014. "Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company," European Journal of Operational Research, Elsevier, vol. 233(2), pages 432-447.
    13. Sahling, Florian & Kayser, Ariane, 2016. "Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty," Omega, Elsevier, vol. 59(PB), pages 201-214.
    14. Min, Hokey & Jeung Ko, Hyun & Seong Ko, Chang, 2006. "A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns," Omega, Elsevier, vol. 34(1), pages 56-69, January.
    15. Govindan, Kannan & Palaniappan, Murugesan & Zhu, Qinghua & Kannan, Devika, 2012. "Analysis of third party reverse logistics provider using interpretive structural modeling," International Journal of Production Economics, Elsevier, vol. 140(1), pages 204-211.
    16. V Jayaraman & V D R Guide & R Srivastava, 1999. "A closed-loop logistics model for remanufacturing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(5), pages 497-508, May.
    17. Khishtandar, Soheila, 2019. "Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design," Applied Energy, Elsevier, vol. 236(C), pages 183-195.
    18. Jimenez, Mariano & Arenas, Mar & Bilbao, Amelia & Rodri'guez, M. Victoria, 2007. "Linear programming with fuzzy parameters: An interactive method resolution," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1599-1609, March.
    19. Aliakbar Hasani & Seyed Hessameddin Zegordi & Ehsan Nikbakhsh, 2015. "Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry," International Journal of Production Research, Taylor & Francis Journals, vol. 53(5), pages 1596-1624, March.
    20. Sawik, Tadeusz, 2016. "Integrated supply, production and distribution scheduling under disruption risks," Omega, Elsevier, vol. 62(C), pages 131-144.
    21. Ron Davis, 2008. "Teaching Note ---Teaching Project Simulation in Excel Using PERT- Beta Distributions," INFORMS Transactions on Education, INFORMS, vol. 8(3), pages 139-148, May.
    22. Geng, Na & Jiang, Zhibin & Chen, Feng, 2009. "Stochastic programming based capacity planning for semiconductor wafer fab with uncertain demand and capacity," European Journal of Operational Research, Elsevier, vol. 198(3), pages 899-908, November.
    23. Ramezanian, Reza & Behboodi, Zahra, 2017. "Blood supply chain network design under uncertainties in supply and demand considering social aspects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 69-82.
    24. Ahmad Rezaee & Farzad Dehghanian & Behnam Fahimnia & Benita Beamon, 2017. "Green supply chain network design with stochastic demand and carbon price," Annals of Operations Research, Springer, vol. 250(2), pages 463-485, March.
    25. Juan-Juan Peng & Jian-Qiang Wang & Xiao-Hui Wu, 2016. "Novel Multi-criteria Decision-making Approaches Based on Hesitant Fuzzy Sets and Prospect Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 621-643, May.
    26. Kumar, V.N.S.A. & Kumar, V. & Brady, M. & Garza-Reyes, Jose Arturo & Simpson, M., 2017. "Resolving forward-reverse logistics multi-period model using evolutionary algorithms," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 458-469.
    27. Dimitris Paraskevopoulos & Elias Karakitsos & Berc Rustem, 1991. "Robust Capacity Planning Under Uncertainty," Management Science, INFORMS, vol. 37(7), pages 787-800, July.
    28. Garrido, Rodrigo A. & Lamas, Patricio & Pino, Francisco J., 2015. "A stochastic programming approach for floods emergency logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 18-31.
    29. Cardoso, Sónia R. & Barbosa-Póvoa, Ana Paula F.D. & Relvas, Susana, 2013. "Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 226(3), pages 436-451.
    30. Jan A. Van Mieghem, 2003. "Commissioned Paper: Capacity Management, Investment, and Hedging: Review and Recent Developments," Manufacturing & Service Operations Management, INFORMS, vol. 5(4), pages 269-302, July.
    31. Gou, Qinglong & Liang, Liang & Huang, Zhimin & Xu, Chuanyong, 2008. "A joint inventory model for an open-loop reverse supply chain," International Journal of Production Economics, Elsevier, vol. 116(1), pages 28-42, November.
    32. Vahdani, Behnam & Tavakkoli-Moghaddam, Reza & Modarres, Mohammad & Baboli, Armand, 2012. "Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1152-1168.
    33. Hafezi, Maryam & Zolfagharinia, Hossein, 2018. "Green product development and environmental performance: Investigating the role of government regulations," International Journal of Production Economics, Elsevier, vol. 204(C), pages 395-410.
    34. Hamed Soleimani & Mirmehdi Seyyed-Esfahani & Mohsen Akbarpour Shirazi, 2016. "A new multi-criteria scenario-based solution approach for stochastic forward/reverse supply chain network design," Annals of Operations Research, Springer, vol. 242(2), pages 399-421, July.
    35. Paksoy, Turan & Bektas, Tolga & Özceylan, Eren, 2011. "Operational and environmental performance measures in a multi-product closed-loop supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(4), pages 532-546, July.
    36. Zolfagharinia, Hossein & Haughton, Michael, 2018. "The importance of considering non-linear layover and delay costs for local truckers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 331-355.
    37. Fatemeh Zare & M.M. Lotfi, 2015. "A possibilistic mixed-integer linear programme for dynamic closed-loop supply chain network design under uncertainty," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 21(1), pages 119-140.
    38. Alumur, Sibel A. & Nickel, Stefan & Saldanha-da-Gama, Francisco & Verter, Vedat, 2012. "Multi-period reverse logistics network design," European Journal of Operational Research, Elsevier, vol. 220(1), pages 67-78.
    39. Haddadsisakht, Ali & Ryan, Sarah M., 2018. "Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax," International Journal of Production Economics, Elsevier, vol. 195(C), pages 118-131.
    40. Mirzapour Al-e-hashem, S.M.J. & Malekly, H. & Aryanezhad, M.B., 2011. "A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty," International Journal of Production Economics, Elsevier, vol. 134(1), pages 28-42, November.
    41. Ouhimmou, Mustapha & Nourelfath, Mustapha & Bouchard, Mathieu & Bricha, Naji, 2019. "Design of robust distribution network under demand uncertainty: A case study in the pulp and paper," International Journal of Production Economics, Elsevier, vol. 218(C), pages 96-105.
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