考虑切换时间和成本的混装线排序优化  

Sequencing Optimization of Mixed Model Assembly Line Regarding Switching Time and Cost

作　　者：戴隆州 吴永明[1] 罗利飞[1] 徐艳霞[1] 张晗[1] 

机构地区：[1]贵州大学现代制造技术教育部重点实验室,贵州贵阳550025

出　　处：《科技广场》2017年第1期6-10,共5页Science Mosaic

基　　金：国家自然科学基金资助项目(51505094);贵州省科学技术基金计划项目(黔科合基础(2016)1037);贵州省应用基础研究计划重大项目[黔科合JZ字(2014)2001];贵州大学引进人才科研项目[贵大人基合字(2014)60号];贵州大学研究生创新基金资助

摘　　要：本文针对降低混装线企业中的生产成本问题,考虑工人闲置与超载时间内成本的差异,以最小化工作站内闲置与超载成本,最小化产品间的总切换时间为优化目标建立混装线投产排序数学模型。并设计了一种新改进的粒子群算法,算法中引入反向学习优化初始种群分布,从而提高了算法的寻优效率。为避免算法陷入局部收敛,将Baldwinian学习策略引入该算法中,增加了种群中粒子的多样性,提高了种群的全局搜索能力。结合具体实例的验证表明,该改进粒子群算法能有效地解决混装线投产排序问题。Aiming at the problem of reducing production cost in the mixed-model assembly line, a sequencing model with optimized objective of minimizing simultaneously the total cost of idle-time and over-time as well as the total product set-up time is built while concerning different cost of workers in idle/overload time and shortening the production cycle of the products. Moreover, a new and improved particle swarm optimization（PSO） algorithm is proposed to solve the model. In the algorithm, the initial population distribution is introduced into the reverse learning optimization, which can improve the search efficiency of the algorithm. In order to avoid optimal solution plunging into a local convergence, the Baldwinian learning strategy was introduced into the algorithm to increase the diversity of particles in the population and improve the global searching ability of the population. The results of specific examples of verification show that the improved PSO algorithm can effectively solve the problem of mixed-model assembly lines sequencing problem.

关 键 词：混装线 排序问题 粒子群算法 

分 类 号：F273[经济管理—企业管理] TP301[经济管理—国民经济]