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Dual-Layer Multi-Objective Particle Swarm Optimization Algorithm for Partial Destructive Incomplete Disassembly Line Balancing Problem
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DOI：10.1109/TASE.2026.3675797.png)
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Abstract
En 中文
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. Note to Practitioners—Disassembly plays a key role in improving resource utilization and reducing environmental pollution. The disassembly line balancing problem has attracted significant attention from enterprises due to its importance in improving recycling efficiency and maximizing profits. Components of end-of-life products exhibit various attributes, such as hazardous materials, low disassembly feasibility, and low recycling value, which necessitate distinct disassembly modes to address these characteristics and enhance profitability. To meet the enterprises’ needs for increased profits and reduced energy consumption, a novel partial destructive incomplete disassembly line balancing problem is proposed, combining conventional disassembly, destructive disassembly, and non-disassembly modes. Additionally, a dual-layer multi-objective particle swarm algorithm is designed, incorporating a dual-layer learning-based particle updating mechanism and a dual-layer local search. The experimental results show that the proposed algorithm effectively solves PDI-DLBP, demonstrating significant advantages in improving performance and efficiency. Therefore, practitioners can benefit from non-dominated schemes characterized by improved benefits and enhanced energy efficiency.
Keywords:
Disassembly line balancing problem (DLBP)
multi-objective particle swarm optimization (MOPSO)
partial disassembly
destructive disassembly
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