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Optimizing Multi-period Green Electric Vehicle Battery Recycling Network: Integrating Winner Determination Within a 4PL Framework

Mingqiang Yin et al.

IEEE Transactions on Engineering Management2026https://doi.org/10.1109/tem.2026.3658193article
AJG 3ABDC A
Weight
0.46

What the paper says

This article provides practical guidance for engineering managers and policymakers to design cost-efficient and sustainable REVB-RNs. Engineering managers should adopt multi-period network structure, incorporate low-carbon regulations and address heterogeneity of REVB capacities through a collaborative framework that integrates the EVM's proprietary recycling network with 3PRs. Specifically, embedding WD of CRA at the tactical level with REVB-RN design at the strategic level can minimize the total cost under uncertainty. Capacity-based categorization of REVBs enables more specialized and granular management of REVB-RNs. For policymakers, effective inducements for responsible recycling include setting appropriate carbon caps and taxes and imposing penalties for unrecycled REVBs. Overall, the proposed collaborative framework can support a more sustainable and cost-efficient ecosystem for related departments to better recycle REVBs. This study contributes to SDGs 9, 12 and 13.

4 citations

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https://doi.org/https://doi.org/10.1109/tem.2026.3658193

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@article{mingqiang2026,
  title        = {{Optimizing Multi-period Green Electric Vehicle Battery Recycling Network: Integrating Winner Determination Within a 4PL Framework}},
  author       = {Mingqiang Yin et al.},
  journal      = {IEEE Transactions on Engineering Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tem.2026.3658193},
}

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Optimizing Multi-period Green Electric Vehicle Battery Recycling Network: Integrating Winner Determination Within a 4PL Framework

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.