针对拆卸需求零件和危害零件的不完全拆卸线平衡问题, 构建了优化拆卸序列长度、工作站数目、空闲时间均衡指标和拆卸成本的多目标不完全拆卸线平衡模型; 为适应问题的离散性、多目标、多约束特性, 提出了一种基于 Pareto 解集的目标驱动离散布谷鸟搜索算法. 该算法首先建立模型与鸟窝位置、鸟蛋属性的映射关系, 以此制定莱维飞行操作、巢寄生操作的离散化规则; 然后通过以目标为导向的驱动操作实现单目标深度优化与多目标协同优化; 为获得分布性良好的拆卸方案, 采用拥挤距离机制筛选外部档案中的非劣解. 对不同规模的3个实例与19个基准算例进行实验, 验证了该算法的有效性和优越性; 以某打印机的不完全拆卸为例, 采用文中模型和算法进行不完全拆卸线平衡多目标优化, 为决策者提供了侧重点不同的9种拆卸方案.%Aimed at the partial disassembly line balancing problem(PDLBP) for removing the parts in de-mand and hazardous parts, the multi-objective mathematical model of PDLBP considering four optimization goals of the disassembly path length, the number of workstations, idle time equilibrium index and the disas-sembly cost was constructed. In order to adapt to the discrete, multi-objective and multi-constrained charac-teristics of the proposed method, a goal-driven discrete cuckoo search algorithm based on the Pareto set was designed. First, the proposed algorithm constructed a mapping relationship of the mathematical model with the nest location and the egg attributes which was used for formulating the discretization rules to Lévy flight operation and the operation of the nest parasitism. Then, the depth optimization of a single objective and the collaborative optimization of the multiple objectives were realized through goal-driven operation. The crowding distance mechanism was introduced to select the non-inferior solutions in the external file. The validity and superiority of the proposed algorithm were verified by testing 3 instances and 19 benchmark in-stances on different scales. Finally, the proposed model and algorithm were applied for the partial disassem-bly of a printer which provides decision makers with 9 kinds of different disassembly schemes.
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