Reverse logistics for electric vehicles under uncertainty: An intelligent emergency management approach

The frequency and intensity of global disasters, including the COVID-19 pandemic, and natural disasters such as earthquakes, floods, and wildfires, are increasing, necessitating effective emergency logistics management. Climate change significantly contributes to these events, emphasizing the importance of limiting human and environmental impacts. The transportation sector, particularly the automobile industry, ranks second in global carbon emissions, highlighting the need to adopt electric vehicles (EVs) to reduce emissions and minimize the impact of climate change. However, this has led to an increase in demand for lithium-ion batteries. During emergencies, end-of-life (EOL) battery management through reverse logistics is essential because recycling EOL batteries can recover valuable raw materials, decrease landfill waste and costs, and support environmental sustainability. This study proposed a two-phase method for intelligent emergency EV battery reverse logistics management. The first phase employed machine learning to address unpredictable battery demands, whereas the second phase proposed a multi-objective model to minimize carbon emissions through efficient order allocation during uncertain emergencies. The model considers carbon emissions and defect rates as sources of uncertainty, current regulations, and customer environmental awareness. The model is solved using the weighted sum and ε-constraint methods, resulting in non-dominant solutions. The findings indicate that combining the selection of third-party reverse logistics providers (3PRLPs) with optimal order allocation for recycling old batteries during emergencies effectively minimizes environmental impacts and combats climate change.

• Record URL:
  • https://doi.org/10.1016/j.tre.2024.103806
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1366554524003971
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Kumar Jauhar, Sunil
  • Singh, Apoorva
  • Kamble, Sachin
  • Tiwari, Sunil
  • 0000-0002-0499-2794
  • Belhadi, Amine
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 103806
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 192
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: Electric vehicles; Emergency management; Environmental impacts; Lithium batteries; Logistics; Machine learning; Uncertainty
• Subject Areas: Data and Information Technology; Environment; Highways; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01935687
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 31 2024 4:20PM