求解考虑机器调整时间的并行机分批优化调度问题

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。

Solve Batch Optimization Scheduling Problem of Parallel Machine Considering Machine Adjustment Time

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.