Abstract: Disassembly is one of the major activities performed in product recovery. The disassembly efficiency directly affects the remanufacturing cost. Taking into account the total disassembly time, we construct an optimization model for multi-objective sequence-dependent disassembly line balancing problem (SDDLBP). Then an adaptive evolutionary variable neighborhood search algorithm is proposed to solve this model. In the proposed algorithm, the initial population is generated by a combination strategy of a GNIS heuristic algorithm and one point right operator, and the individual is selected for evolution by the tournament selection method. In local search process, an adaptive selection strategy is designed for neighborhoods, and a global learning mechanism based on crossing operator is proposed to accelerate the algorithm to jump out of the local optimum. Finally, the performance of the proposed model and algorithm is evaluated by a set of benchmark instances.

Key words: sequence-dependent, disassembly line balancing problem, multi-objective, variable neighborhood search algorithm

摘要： 拆卸是产品回收过程最关键环节之一,拆卸效率直接影响再制造成本。本文在分析现有模型不足基础上,考虑最小化总拆卸时间,建立多目标顺序相依拆卸线平衡问题优化模型,并提出了一种自适应进化变邻域搜索算法。所提算法引入种群进化机制,并采用一种组合策略构建初始种群,通过锦标赛法选择个体进化;在局部搜索时,设计了邻域结构自适应选择策略,并采用基于交叉的全局学习机制加速跳出局部最优,以提高算法寻优能力。对比实验结果,证实了所提模型的合理性以及算法的高效性。

关键词: 顺序相依, 拆卸线平衡, 多目标, 变邻域搜索算法