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A stochastic reverse logistics production routing model with environmental considerations

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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.

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Author information

Authors and Affiliations

  1. Pan Shulun Honors College, Shanghai Lixin University of Accounting and Finance, 995 Shangchuan Road, Shanghai, 201209, China

    Yiqiang Zhang

  2. Division of Engineering, New York University Abu Dhabi, 129188, Saadiyat Island, Abu Dhabi, United Arab Emirates

    Hussam Alshraideh & Ali Diabat

  3. Department of Industrial Engineering, Jordan University of Science and Technology, Irbid, Jordan

    Hussam Alshraideh

  4. Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA

    Ali Diabat

Authors
  1. Yiqiang Zhang
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  2. Hussam Alshraideh
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  3. Ali Diabat
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Corresponding author

Correspondence to Ali Diabat.

Appendix A: Simulated network modeling parameters

Appendix A: Simulated network modeling parameters

See Tables 7, 8, 9, 10 and 11.

Table 7 Used values for the simulated network modeling scalar parameters
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Table 8 Transportation vehicles emission and travel cost parameters
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Table 9 Demand for the four time periods planning horizon
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Table 10 Initial inventory at the manufacturing, remanufacturing and customer facilities
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Table 11 Demand under the three scenarios used for the four time periods planning horizon
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Zhang, Y., Alshraideh, H. & Diabat, A. A stochastic reverse logistics production routing model with environmental considerations. Ann Oper Res 271, 1023–1044 (2018). https://doi.org/10.1007/s10479-018-3045-2

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  • DOI: https://doi.org/10.1007/s10479-018-3045-2

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