Dual-Layer Multi-Objective Particle Swarm Optimization Algorithm for Partial Destructive Incomplete Disassembly Line Balancing Problem

Disassembly lines play a crucial role in the recycling and reuse of end-of-life (EOL) products to promote environmental protection and economic benefits. However, the diverse characteristics of EOL products necessitate the adoption of different disassembly modes, posing challenges to existing models that rely solely on non-destructive or complete disassembly. Thus, a dual-layer multi-objective particle swarm algorithm (DMOPSO) is proposed to solve the partial destructive incomplete disassembly line balancing problem (PDI-DLBP), with optimization of economic profit and energy consumption. Firstly, a tailored encoding-decoding scheme is designed for disassembly sequence, mode selection, and task assignment to obtain high-quality disassembly schedules. Then, a dual-layer learning-based particle updating mechanism is designed, incorporating a constraint-based mode updating operator and a multi-source segment learning strategy to enhance search efficiency and optimization stability. Additionally, a dual-layer local search based on molecular forces is introduced to enhance the exploitation ability. The proposed DMOPSO algorithm is applied to several test cases and a real-world disassembly line of TV. Experiment results show that DMOPSO outperforms state-of-the-art algorithms in solving PDI-DLBP. Specifically, the proposed DMOPSO achieves a 5.7% increase in disassembly profit and a 6.5% reduction in disassembly energy consumption compared to the existing best algorithm for solving the real-world case.