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Closed-Loop Decision-making Framework for Electric Vehicle Battery Recycling: Synchronizing Reverse Logistics Network Optimization with Disassembly Line Design
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DOI：10.1016/j.ijpe.2026.110057.png)
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Abstract
En 中文
• Develop an integrated decision-making framework for joint synchronizing optimization of reverse logistics network and disassembly line design in waste battery recycling. • A machine learning-based decomposition-integration method is developed to achieve electric vehicle sales forecasting for waste battery recycling management. • The impact of worker levels and multi-operator workstations on disassembly costs is taken into consideration. • Design an improved multi-stage adaptive large neighborhood search (MS-ALNS) algorithm to solve the joint optimization model. • The performance and adaptability of the formulated model are validated through a practical case in the Yangtze River Delta region.
Keywords:
reverse logistics
disassembly line design
waste battery recycling
machine learning
adaptive large neighborhood search
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