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<p>Disassembly is a critical link in the recycling process of end-of-life (EOL) products. To address the balancing problem of U-shaped selective disassembly lines, this study defines three core optimization objectives: minimizing the number of workstations, the disassembly smoothness coefficient, and carbon emissions, and establishes a multi-objective coupled optimization model. To achieve the synergistic improvement of disassembly efficiency and low-carbon performance, an Adaptive Roulette Wheel Flower Pollination Algorithm (ARFPA) is proposed in this paper. Developed based on the traditional Flower Pollination Algorithm (FPA), ARFPA introduces innovations in both optimization operators and the roulette selection mechanism: the four designed optimization operators enhance the population's propagation ability and the algorithm's search capability, while the roulette selection mechanism dynamically adjusts the optimization strategy by calculating the fitness values of each optimization operator. Finally, the effectiveness and superiority of the proposed model are validated by solving the disassembly plan for battery packs on the U-shaped disassembly line.</p>

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Optimization of U-shaped disassembly line balancing based on adaptive roulette-flower pollination algorithm

  • 期刊论文

  • 发表日期：

    2026年7月26日

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  • Jiaqi Hu,
  • Yonggao Fu,
  • Qingbao Yan,
  • Ziao Zhou

摘要

Disassembly is a critical link in the recycling process of end-of-life (EOL) products. To address the balancing problem of U-shaped selective disassembly lines, this study defines three core optimization objectives: minimizing the number of workstations, the disassembly smoothness coefficient, and carbon emissions, and establishes a multi-objective coupled optimization model. To achieve the synergistic improvement of disassembly efficiency and low-carbon performance, an Adaptive Roulette Wheel Flower Pollination Algorithm (ARFPA) is proposed in this paper. Developed based on the traditional Flower Pollination Algorithm (FPA), ARFPA introduces innovations in both optimization operators and the roulette selection mechanism: the four designed optimization operators enhance the population's propagation ability and the algorithm's search capability, while the roulette selection mechanism dynamically adjusts the optimization strategy by calculating the fitness values of each optimization operator. Finally, the effectiveness and superiority of the proposed model are validated by solving the disassembly plan for battery packs on the U-shaped disassembly line.