Human-robot cooperation two-sided partial disassembly line balancing problem

Mengling Chu and Weida Chen

International Journal of Production Research, 2025, vol. 63, issue 12, 4478-4503

Abstract: Considering the complexities, risks, and uncertainties of disassembling large end-of-life (EOL) products such as cars and buses, a two-sided human-robot disassembly line can utilise both sides of the workstations to enhance efficiency, improve safety, and increase revenue. This paper develops a human-robot cooperation two-sided partial disassembly line balancing model (TPDLB-HRC) to minimise energy consumption and maximise net revenue by addressing four interrelated sub-problems: planning disassembly sequences, selecting disassembly tasks, assigning tasks to mated-stations, and allocating human-robot resources. In addition, a new reinforcement-learning multi-objective evolutionary algorithm based on decomposition (NRL-MOEA/D) is developed, integrating an encoding/decoding scheme, reinforcement learning, problem characteristics, and coevolution between sub-problems to address the above challenges. The effectiveness and superiority of the designed NRL-MOEA/D in solving various cases are tested by comparing it with eleven algorithms. Finally, the applicability of the proposed method is verified by a series of EOL examples, and trade-offs are made under different recycling profits to guide decision-makers in constructing disassembly schemes in real situations.

Date: 2025
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DOI: 10.1080/00207543.2025.2452385

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