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

Sunil Kumar Jauhar, Apoorva Singh, Sachin Kamble, Sunil Tiwari and Amine Belhadi

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 192, issue C

Abstract: 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.

Keywords: Emergency Logistics Operations; Battery Industry; Carbon Emission Reduction; Machine Learning; Multi-Objective Optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1366554524003971
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transe:v:192:y:2024:i:c:s1366554524003971

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/bibliographic
http://www.elsevier. ... 600244/bibliographic

DOI: 10.1016/j.tre.2024.103806

Access Statistics for this article

Transportation Research Part E: Logistics and Transportation Review is currently edited by W. Talley

More articles in Transportation Research Part E: Logistics and Transportation Review from Elsevier
Bibliographic data for series maintained by Catherine Liu ().