Wang Kaipu, Li Xinyu, Gao Liang, Li Peigen, Sutherland John W
IEEE Trans Cybern. 2022 Aug;52(8):7415-7426. doi: 10.1109/TCYB.2020.3042896. Epub 2022 Jul 19.
Disassembly lines are the most effective way to address large-scale value recovery from end-of-life (EOL) products. Disassembly line balancing (DLB) greatly affects the economics and throughput of EOL product processing. Complete disassembly is generally not suitable for disassembly enterprises; most often, the maximum profit is realized through partial disassembly. Thus, this article proposes a partial disassembly method and establishes a new DLB model that addresses both economic benefits and environmental impacts. The objective of the model is to maximize the effectiveness of workers, increase profit, reduce energy consumption, and balance the loads of workers. Moreover, the model considers the impact of disassembly face and tool changes on the disassembly process. A discrete multiobjective artificial bee colony (MOABC) algorithm is developed, and it takes the precedence constraints into account to obtain the Pareto solutions. The MOABC algorithm is applied to the disassembly lines of two real-world EOL products, including those of an LCD TV and a refrigerator. Experiments show that the performance of the MOABC algorithm is better than those of five well-known multiobjective algorithms. The proposed model and method can provide multiple disassembly schemes for decision makers of disassembly enterprises based on their preferences.
拆解线是从报废（EOL）产品中实现大规模价值回收的最有效方式。拆解线平衡（DLB）对EOL产品处理的经济性和产量有很大影响。完全拆解通常不适用于拆解企业；大多数情况下，通过部分拆解实现利润最大化。因此，本文提出了一种部分拆解方法，并建立了一个兼顾经济效益和环境影响的新DLB模型。该模型的目标是使工人效率最大化、增加利润、降低能耗并平衡工人负荷。此外，该模型考虑了拆解面和工具更换对拆解过程的影响。开发了一种离散多目标人工蜂群（MOABC）算法，该算法考虑了优先约束以获得帕累托解。将MOABC算法应用于两种实际EOL产品的拆解线，包括液晶电视和冰箱的拆解线。实验表明，MOABC算法的性能优于五种著名的多目标算法。所提出的模型和方法可以根据拆解企业决策者的偏好为他们提供多种拆解方案。
