计算机应用研究2018，Vol.35Issue(1)：145-149,5.DOI:10.3969/j.issn.1001-3695.2018.01.030
多目标粒子群算法在混装线再平衡中的应用
Multi-objective particle swarm algorithm in mixed-model assembly line rebalancing
摘要
Abstract
In the enterprise,with the innovation of products,assembly tasks on the assembly line have changed,resulting in the balance of the assembly line lost the balance.Aiming at the impact of these changes brought to the assembly line,this paper studied this problem and established a mathematical model for mixed-model assembly line rebalancing,in which the optimization objectives were minimizing the cycle time and station load,and the adjustment cost of completing the new assembly tasks by operators.Moreover,this paper proposed a new and improved multi-objective particle swarm optimization (MOPSO)algorithm to solve the model.The algorithm used dynamic crowding distance to update the Pareto solution set and guide the selection of global optimal solution,made solution more evenly distribute while maintaining the size of the solution set.Besides,it introduced the particle mutation strategy into the algorithm to improve the global searching ability of the population.Finally,with specific examples of the verification results show that the improved MOPSO algorithm can effectively solve the problem of mixed-model assembly lines rebalancing.关键词
混装线/再平衡/多目标优化/粒子群算法Key words
mixed-model assembly line/rebalancing/multi-objective optimization/particle swarm algorithm分类
信息技术与安全科学引用本文复制引用
戴隆州,吴永明,李少波,罗利飞..多目标粒子群算法在混装线再平衡中的应用[J].计算机应用研究,2018,35(1):145-149,5.基金项目
国家自然科学基金资助项目(51505094) （51505094）
贵州省科学技术基金计划项目(黔科合基础(2016)1037) （黔科合基础(2016）
贵州省应用基础研究计划重大项目[黔科合JZ字(2014)2001] （2014）
贵州大学引进人才科研项目[贵大人基合字(2014)60号] （2014）
贵州大学研究生创新基金资助项目 （）
