摘要
针对混流U型拆卸线平衡排序问题,考虑拆卸时间不确定,建立了该问题最小拆卸线平均闲置率、尽早拆卸危害和高需求零部件、最小化平均方向改变次数的多目标优化模型,并提出一种基于分解和动态邻域搜索的混合多目标进化算法(Hybrid Multi-objective Evolutionary Algorithm Based on Decomposition,HMOEA/D)。该算法通过采用弹性任务分配策略、动态邻域结构和动态调整权重以保证解的可行性并搜索得到分布较好的非劣解集。最后,仿真求解实验设计技术(DOE)生成的测试算例,结果表明HMOEA/D较其它算法能得到更接近Pareto最优、分布更好的近似解集。
To solve the mixed model U-shaped disassembly line balancing and sequencing problem with stochastic task times, a mathematical model is established aiming at minimizing mean line idle rates, removing hazardous and high-demand parts as early as possible and minimizing the mean number of part removal direction changes. Besides, a hybrid multi-objective evolutionary algorithm based on decomposition and dynamic neighborhood search method（HMOEA/D） is proposed to solve the problem. In HMOEA/D, a flexible tasks assignment strategy, dynamic neighborhood structure and dynamic weight vector adjustment are adopted to ensure the solutions＇ feasibil- ity and the distribution of the non-dominated set. Finally, the algorithm is tested on benchmark instances generated by using Design of Experiment（DOE）techniques. Experimental results show that HMOEA/D can get an approximation set closer to the Pareto optimal front and distributed better when compared to other algorithms.
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2017年第9期52-61,共10页
Operations Research and Management Science
基金
国家自然科学基金资助项目(Nos.71471151
61573264)
