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A stochastic reverse logistics production routing model with environmental considerations

Author

Listed:
  • Yiqiang Zhang

    (Shanghai Lixin University of Accounting and Finance)

  • Hussam Alshraideh

    (New York University Abu Dhabi
    Jordan University of Science and Technology)

  • Ali Diabat

    (New York University Abu Dhabi
    New York University)

Abstract

Growing global concerns of environmental problems have led to the emergence of policies and regulations to control carbon emissions in the industrial sector. These regulations must be taken into consideration to obtain optimal operational decisions on production, inventory and routing in supply chain network models. In this study, we consider the reverse logistics supply chain model with a remanufacturing option to reduce carbon emissions. We aim at providing optimal production, inventory and delivery quantities along with delivery and pickup routes under a carbon cap-and-trade emissions policy. We provide a mathematical formulation of the problem that considers heterogeneous transportation fleets and allows for lost sales under the cap-and-trade carbon emissions policy. The proposed mathematical model is provided in a deterministic and a two-stage stochastic versions to account for demand uncertainty. Proposed formulations are demonstrated through a simulated reverse logistics supply chain with added sensitivity analysis to test for the effect of modeling parameters on the optimal problem solution. Simulation results indicate that carbon policies have significant effect on the supply chain performance with carbon price as the most significant parameter.

Suggested Citation

  • Yiqiang Zhang & Hussam Alshraideh & Ali Diabat, 2018. "A stochastic reverse logistics production routing model with environmental considerations," Annals of Operations Research, Springer, vol. 271(2), pages 1023-1044, December.
    • Handle: RePEc:spr:annopr:v:271:y:2018:i:2:d:10.1007_s10479-018-3045-2
    DOI: 10.1007/s10479-018-3045-2
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    References listed on IDEAS

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    Cited by:

    1. Castellano, Davide & Gallo, Mosè & Grassi, Andrea & Santillo, Liberatina C., 2019. "The effect of GHG emissions on production, inventory replenishment and routing decisions in a single vendor-multiple buyers supply chain," International Journal of Production Economics, Elsevier, vol. 218(C), pages 30-42.
    2. Shraddha Mishra & Surya Prakash Singh, 2022. "A stochastic disaster-resilient and sustainable reverse logistics model in big data environment," Annals of Operations Research, Springer, vol. 319(1), pages 853-884, December.
    3. Zakaria Chekoubi & Wajdi Trabelsi & Nathalie Sauer & Ilias Majdouline, 2022. "The Integrated Production-Inventory-Routing Problem with Reverse Logistics and Remanufacturing: A Two-Phase Decomposition Heuristic," Sustainability, MDPI, vol. 14(20), pages 1-30, October.
    4. Abdelghani Bekrar & Abdessamad Ait El Cadi & Raca Todosijevic & Joseph Sarkis, 2021. "Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective," Sustainability, MDPI, vol. 13(5), pages 1-25, March.
    5. Shuang, Yan & Diabat, Ali & Liao, Yi, 2019. "A stochastic reverse logistics production routing model with emissions control policy selection," International Journal of Production Economics, Elsevier, vol. 213(C), pages 201-216.
    6. Navid Zarbakhshnia & Kannan Govindan & Devika Kannan & Mark Goh, 2023. "Outsourcing logistics operations in circular economy towards to sustainable development goals," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 134-162, January.
    7. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.

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