文献类型：期刊文献

中文题名：基于DEPSO的模糊时间ZW多产品厂间歇调度

英文题名：A study of the DEPSO-based multiproduct plants batch scheduling under uncertainty with zero wait

作者：徐震浩[1];李青青[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：30

期号：12

起止页码：2275

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：20155101686041);Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家自然科学基金项目(61104178;61174040)

语种：中文

中文关键词：粒子群算法;离散差分进化;模糊时间;零等待;间歇调度

外文关键词：particle swarm optimization; discrete differential evolution; uncertain processing time; zero wait; batch scheduling;

摘要：研究以最小化完工时间为目标的模糊加工时间零等待多产品厂间歇调度问题,提出一种基于差分进化粒子群优化(DEPSO)的间歇调度算法.以基本粒子群算法为整体进化框架,采用基于反向学习的方法初始化种群,引入群体极值保持代数作为阈值,利用基于排序的差分进化算法优化粒子个体极值位置,改变粒子的搜索范围,防止粒子陷入局部极值.仿真实验验证了所提算法在解决模糊加工时间零等待多产品厂间歇调度问题上的有效性和优越性.
For minimizing the total flow time of multiproduct plants batch scheduling under uncertain processing time with zero wait, a batch scheduling algorithm based on the differential evolution particle swarm optimization is proposed. With basic particle swarm optimization as the algorithm＇s framework, the opposition-based learning algorithm is used to initialize population, the group extreme is introduced to keep algebra as a threshold, and the permutation-based differential evolution algorithm is used to optimize the individual extremum so as to change the search range and prevent the particle falling into local extremum. The simulation results show the effectiveness of the proposed algorithm in solving multiproduct plants batch scheduling under uncertainty with zero wait.