摘要
基于目前车间调度问题是以单个或整批进行生产加工的并行机调度模型已不再符合实际工况下的车间生产。提出以最小化最大完工时间为优化目标,对遗传差分进化混合算法,灰狼差分进化混合算法进行了比较。为提高加工工件进行分批及分批之后子批的分配与排序效率,该问题是对不同规模的经典并行机调度问题进行求解并展示两种算法的求解,证明了灰狼差分进化混合算法在寻优性能上优于遗传差分进化混合算法,不仅具有更好的解的稳定性,而且具有更高的寻优精度。
Based on the current shop floor scheduling problem,the parallel machine scheduling model for single or batch production processing is no longer consistent with shop floor production under actual operating conditions.Aiming at minimizing the maximum completion time as the optimization goal,the genetic differential evolution hybrid algorithm and the gray wolf differential evolution hybrid algorithm were compared.In order to improve the efficiency of allocation and sequencing of batches and sub-batches after processing batches,the problem is to solve the classical parallel machine scheduling problem of different sizes and to show the solution of two algorithms.The optimization performance is better than the genetic differential evolution hybrid algorithm,not only has better solution stability,but also has higher optimization accuracy.
作者
孙思汉
陶翼飞
董圆圆
张源
王加冕
SUN Si-han;TAO Yi-fei;DONG Yuan-yuan;ZHANG Yuan;WANG Jia-mian(Kunming University of Science and Technology,Kunming 650000,China)
出处
《软件》
2020年第4期20-27,共8页
Software
基金
国家自然科学基金地区基金(批准号:51566006)。
关键词
机器调整时间
灰狼差分进化混合算法
并行机调度
最小化最大完工时间
Machine adjustment time
Grey wolf differential evolution hybrid algorithm
Parallel machine scheduling
Minimizing maximum completion time
