Abstract: Considering the space area constraint of workstation during the actual disassembly process,a multi-objective optimization mathematical model under the space constraint was established including the number of workstations,the idle time equilibrium index,the disassembly cost and the actual working surface difference of the workstation posture four optimization goals.By discretizing the scouting behavior,calling behavior and besieging behavior,a discrete multi-objective improved Wolf pack algorithm was proposed to solve the problem,and the Pareto solution idea and NSGA-Ⅱ crowd distance mechanism were introduced to obtain solutions with high-quality and multi-faceted comprehensive.The effectiveness and superiority of the proposed algorithm were demonstrated by comparing the results of benchmark examples with different scales.The algorithm was used to solve the disassembly of a printer considering spatial constraints,and 10 feasible task allocation schemes were obtained,which showed the superiority of the model considering the space constraint and the proposed algorithm.

Key words: disassembly line balancing, space constraints, multi-objective optimization, wolf pack algorithm

摘要： 考虑实际拆卸过程中的工作站空间面积约束,以最小化工作站数目、空闲时间均衡指标、拆卸成本及工作站实际使用面积极差值为优化目标,建立空间约束下的多目标优化数学模型,提出一种离散多目标改进狼群算法求解。通过对游走行为、召唤行为和围攻行为进行离散化,引入Pareto解集思想及NSGA-Ⅱ拥挤距离机制,获得多个高质量、多方面综合的较优解。通过对不同规模基准算例的求解,对比说明所提算法的有效性和优越性。最后,将该算法用于求解考虑空间约束的某打印机拆卸实例中,得到10组可行的任务分配方案,表明考虑空间约束的模型和所提算法的可行性。

关键词: 拆卸线平衡, 空间约束, 多目标优化, 狼群算法