为了提高仓储车间货物调度的柔性和响应效率,提出一种级联的改进差分进化算法,构建以拣货小车运行时间、货架稳定性及货位的存货能力为资源条件的货位分配和以每批订单中每一货物分配到对应分区的最优货位的最大完工时间为条件的订单重新分批分配的两级目标模型.将拉格朗日插值算法融进标准差分进化算法得到改进算法,分别求解两级目标模型,并将两级求解过程级联,完成级联关系的差分进化算法求解多订单分批分配问题.改进的差分进化算法在自适应地调整差分进化参数基础上,结合拉格朗日插值优化差分进化算法的局部搜索能力,运用局部和全局切换因子动态调整进化方向,提高算法的收敛性能.将改进的差分进化算法应用于求解多订单分批分配问题,实验结果证明,改进的算法优化结果明显优于粒子群优化算法、遗传算法及标准差分进化算法,减少了每一批订单的最大完工时间,有效地均衡工作负载.

In order to improve the flexibility and response efficiency of warehouse dispatching, a cascaded improved differential evolution algorithm is proposed to construct the allocation of goods with the picking trolley running time, shelf stability, and inventory capacity as resource conditions. The maximum completion time for each item in the batch order assigned to the optimal location of the corresponding partition is the two-level target model that is re-batch-allocated for the conditional order. The Lagrangian interpolation algorithm is integrated into the improved algorithm of the standard differential evolution algorithm to solve the two-level target model, and the two-level solution process is cascaded to complete the cascaded differential evolution algorithm to solve the multi-order batch allocation problem. Based on the adaptive adjustment of differential evolution parameters, the improved differential evolution algorithm combines the local search ability of Lagrangian interpolation to optimize the differential evolution algorithm, and uses local and global switching factors to dynamically adjust the evolution direction and improve the convergence performance of the algorithm. The improved differential evolution algorithm is applied to solve the problem of multi-order batch allocation. The experimental results show that the improved algorithm optimization results are better than the particle swarm optimization algorithm, the genetic algorithm, and the standard differential evolution algorithm, which reduces the maximum completion time of each batch of orders and effectively balance the workload.