文献类型：期刊文献

中文题名：基于混合离散粒子群优化的多时间因素作业车间调度研究

英文题名：Research on the job-shop scheduling problem with multi-time constraints based on discrete particle swarm optimization

作者：李继明[1];徐震浩[1];顾幸生[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2015

卷号：25

期号：10

起止页码：980

中文期刊名：高技术通讯

外文期刊名：Chinese High Technology Letters

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD_E2015_2016】；

基金：国家自然科学基金(61104178;61174040)资助项目

语种：中文

中文关键词：作业车间调度;调整时间;运输时间;提前/拖期;混合离散粒子群

外文关键词：job-shop scheduling, setup time, transit time, earliness/tardiness, hybrid discrete particleswarm

摘要：应用粒子群优化(PSO)进行了考虑机器调整时间、工件运输时间以及提前/拖期惩罚的作业车间调度问题的研究,分析了各时间约束对调度的影响,在此基础上设计了一种解决多时间约束调度问题的混合离散粒子群(HDPSO)算法。该算法在初始阶段采用反向学习机制初始化以提高初始解质量,引入记忆池的概念,在每次迭代中利用记忆池中精英解对当代种群搜索加以指导,以增加粒子与优秀群体间的交流并提高收敛速度及跳出局部最优的能力,最后采用一种针对问题的变邻域搜索策略提高了算法收敛精度。实例仿真验证了该算法的有效性。
The job-shop scheduling considering the ness/tardiness punishment was studied by the time constraints of processing time, setup time, transit time and earli- application of particle swarm optimization （ PSO）, and the influences of the constraints on the scheduling were analyzed. Then, a hybrid discrete （HD） PSO （HDPSO） algorithm tbr solving the job-shop scheduling with multi-time constraints was designed. The algorithm uses the opposition-based learning to initialize population to improve the quality of the initial population. It also implants the memory mecha- nism into the discrete PSO to speed up convergencel At last the algorithm adopts a modified variable neighborhood search （VNS） to strengthen the local search ability. The effectiveness of the proposed algorithm was demonstrated by the experiments on different simulation examnles.