Considering the imperfect cooperation among workers in the two-sided partial disassembly line balancing problem and the corresponding multi-modal multi-objective solution algorithm

In existing research on the balance of the two-sided disassembly line, the additional disassembly time caused by the imperfect cooperation of workers on both sides of the disassembly line is often disregarded. This oversight hinders current research from accurately reflecting the actual disassembly situation. In response to the above issue, this study proposes a two-sided partial disassembly line balancing problem with imperfect worker cooperation (TPDLBP-IWC). Furthermore, in existing studies, the problem of disassembly line balancing is typically treated as a multi-objective optimization problem and solved using multi-objective evolutionary algorithms, leading to a scarcity of equivalent Pareto optimal solutions. A lack of equivalent Pareto optimal solutions in practical applications may bias decision-makers' overall understanding of the issue, resulting in unnecessary economic losses. Addressing the aforementioned problem, this research initially considers the disassembly line balancing problem as a multi-modal multi-objective optimization problem and proposes a multi-modal multiobjective evolutionary algorithm (MMEA-DLBP) tailored for the disassembly line balancing problem. To find a more comprehensive set of optimal solutions and enhance the algorithm's performance, this paper introduces for the first time an Equilibrium Monte Carlo Tree Initialization (EMCI) approach from the perspective of improving population diversity. EMCI can effectively increase the initial population's coverage in the solution space, thereby enhancing the diversity of the initial population. Secondly, a Double Pareto Elite Selection Strategy Based on Disassembly Sequence Distance (DPES-DSD) is proposed, which effectively maintains the diversity of the population in both the decision and objective spaces and assists the algorithm in obtaining more equivalent Pareto optimal solutions. Experimental results show that MMEA-DLBP can effectively solve the disassembly line balancing problem. Compared with other algorithms, MMEA-DLBP provides a more comprehensive set of highquality disassembly solutions, thereby offering decision-makers a wider range of choices.