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

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.