Ensemble artificial bee colony algorithm with Q-learning for scheduling Bi-objective disassembly line

Ren, Yaxian; Gao, Kaizhou; Fu, Yaping; Li, Dachao; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.asoc.2024.111415

Applied Soft Computing

2024

DOI: 10.1016/j.asoc.2024.111415

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Ensemble artificial bee colony algorithm with Q-learning for scheduling Bi-objective disassembly line

Yaxian Ren,

Kaizhou Gao,

Yaping Fu

et al.

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Cited by 3 publications

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An artificial bee colony optimization algorithms for solving fuzzy capacitated logistic distribution center problem

Ayid,

Zakaraia,

Eltoukhy

2024

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An artificial bee colony optimization algorithms for solving fuzzy capacitated logistic distribution center problem

Ayid,

Zakaraia,

Eltoukhy

2024

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Energy Estimation and Production Scheduling in Job Shop Using Machine Learning

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Artificial Intelligence in Electric Vehicle Battery Disassembly: A Systematic Review

Ai,

Nee,

Ong

2024

Automation

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The rapidly increasing adoption of electric vehicles (EVs) globally underscores the urgent need for effective management strategies for end-of-life (EOL) EV batteries. Efficient EOL management is crucial in reducing the ecological footprint of EVs and promoting a circular economy where battery materials are sustainably reused, thereby extending the life cycle of the resources and enhancing overall environmental sustainability. In response to this pressing issue, this review presents a comprehensive analysis of the role of artificial intelligence (AI) in improving the disassembly processes for EV batteries, which is integral to the practical echelon utilization and recycling process. This paper reviews the application of AI techniques in various stages of retired battery disassembly. A significant focus is placed on estimating batteries’ state of health (SOH), which is crucial for determining the availability of retired EV batteries. AI-driven methods for planning battery disassembly sequences are examined, revealing potential efficiency gains and cost reductions. AI-driven disassembly operations are discussed, highlighting how AI can streamline processes, improve safety, and reduce environmental hazards. The review concludes with insights into the future integration of electric vehicle battery (EVB) recycling and disassembly, emphasizing the possibility of battery swapping, design for disassembly, and the optimization of charging to prolong battery life and enhance recycling efficiency. This comprehensive analysis underscores the transformative potential of AI in revolutionizing the management of retired EVBs.

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Ensemble artificial bee colony algorithm with Q-learning for scheduling Bi-objective disassembly line

Cited by 3 publications

References 49 publications

An artificial bee colony optimization algorithms for solving fuzzy capacitated logistic distribution center problem

An artificial bee colony optimization algorithms for solving fuzzy capacitated logistic distribution center problem

Energy Estimation and Production Scheduling in Job Shop Using Machine Learning

Artificial Intelligence in Electric Vehicle Battery Disassembly: A Systematic Review

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