一种混合人工蜂群算法求解顺序相依拆卸线平衡问题

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.所提出算法在观察蜂跟随阶段采用分阶段选择评价法,以便更好地区分蜜源;在侦查蜂开采阶段构建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,提高了寻优效率.对比实验结果验证了模型的有效性和算法的优越性.

A hybrid artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem

Disassembly line balancing problems directly affect the reverse/remanufacturing cost. A multi-objective sequence-dependent disassembly line balancing problem(SDDLBP) optimization model is established for minimizing the number of opened workstations, minimizing the total disassembly time, distributing the idle times across the opened workstations evenly, and processing the hazardous components and high-demand parts early. Then a hybrid artificial bee colony(HABC) algorithm is proposed to solve this problem. In the onlooker bee phase, a multi-stage evaluation method is presented to distinguish food sources better. In the scout bee phase, a search strategy based on the global learning mechanism is constructed to improve the exploitation ability of the scouts. When the bees search for new food sources, the reduced variable neighborhood search(RVNS) strategy is used to promote the search efficiency. Finally, computational results demonstrate the effectiveness of the proposed model and algorithm.