Collaborative reverse logistics network for electric vehicle batteries management from sustainable perspective

Area of Science:

• Environmental Science and Engineering
• Operations Research and Management Science
• Circular Economy

Background:

• Growing attention to environmental protection and carbon neutrality necessitates effective waste electric vehicle battery (WEVB) management.
• The reverse logistics process for WEVBs is critical for recycling, cost reduction, and efficiency, yet presents significant design challenges.
• Uncertainty in the quantity and quality of returned WEVBs complicates reverse logistics network design (RLND).

Purpose of the Study:

• To design a multi-participant-based reverse logistics network (RLN) for WEVBs, incorporating recycling and remanufacturing processes.
• To develop a fuzzy optimization model for determining optimal facility numbers and locations within the WEVB RLN.
• To promote sustainable development, reduce environmental pollution, and account for carbon emissions and different WEVB types.

Main Methods:

• Development of a multi-participant reverse logistics network framework for WEVBs.
• Application of a fuzzy optimization model to address uncertainties in WEVB RLND.
• Case study analysis in Chongqing to evaluate the proposed model's performance.

Main Results:

• The proposed fuzzy optimization model effectively determines facility placement for WEVB recycling and remanufacturing.
• The RLND approach demonstrated significant cost reduction, profit increase, and efficiency improvement in the case study.
• The model successfully incorporates considerations for carbon emissions and diverse WEVB types.

Conclusions:

• The designed RLN and fuzzy optimization model provide a viable solution for managing WEVBs from a circular economy perspective.
• The findings offer substantial support for decision-making in WEVB recycling and sustainable reverse logistics.
• This research contributes to reducing environmental impact and enhancing economic performance in the WEVB sector.