Multi-objective human-robot collaboration disassembly planning for retired power batteries based on entropy-weight fuzzy hierarchical analysis and differential evolution algorithm

Efficient and safe disassembly of waste electric vehicle batteries (WEVB) is critical for sustainable recycling and resource recovery. However, current studies rarely consider the impact of resource constraints in disassembly scenarios on determining the optimal disassembly sequence. To rationalize the disassembly of used power batteries, this paper proposes a human-robot collaboration (HRC) model aiming to minimize time, cost, hazard index, and energy consumption. Firstly, the dismantling resource space limitation is considered to limit the assigned tasks to one human and one robot, and shop floor operators and robots coordinates to disassembly in the HRC model. In this article, a multi-objective evolutionary algorithm with an adaptive genetic differential evolution algorithm based on successful history (AGDE-ASH), combined with external archiving, is proposed to solve the model which is compared with three other commonly used algorithms for the disassembly of battery of Tesla Model 1 to prove its effectiveness and feasibility. Finally, an entropy-weight fuzzy hierarchical analysis (EWFHA) method, which is a combination of fuzzy analytic hierarchy process (FAHP) and entropy-weight method (EWM), is proposed to evaluate the effectiveness of the proposed approach. Eight Pareto solutions obtained through AGDE-ASH is employed to determine the optimal dismantling order.