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拆卸线平衡问题直接影响回收再制造成本.为此,构建了最小工作站开启数量、最短总拆卸时间、均衡工作站空闲时间、尽早拆卸有危害和高需求零部件的多目标顺序相依拆卸线平衡问题优化模型,提出一种混合人工蜂群算法.所提算法在观察蜂跟随阶段,采用了分阶段选择评价法,以更好的区分蜜源;在侦查蜂开采阶段,构建了基于全局学习的搜索机制,以提高开采能力.蜜蜂寻优过程中,设计了简化变邻域搜索策略,以提高寻优效率.对比实验结果证实了模型的有效性以及算法的优越性.
The problem of balance of disassembly line has a direct impact on the cost of recycling and remanufacturing.Therefore, the optimization of the multi-objective order-dependent disassembly line balancing problem with minimal workstations opening, minimum total disassembly time, equalizing station idle time, dismantling as soon as possible the hazardous and high-demand components Model, a hybrid artificial bee colony algorithm is proposed. The proposed algorithm adopts a phased selection evaluation method to observe the bee-following phase of the bee to better distinguish the nectar source. In the exploratory bee mining phase, a search mechanism based on global learning is constructed, In order to improve the mining ability, a simplified variable neighborhood search strategy is designed to improve the optimization efficiency in the process of bee optimization.The experimental results verify the effectiveness of the model and the superiority of the algorithm.