面向货物装卸需求的越库仓门分配和货车排序

A Research on Dock Assignment and Truck Sequence in a Cross-docking System Considering the Unloading / Loading Sequences of Cargos

在货物种类多、批量少的越库调度系统中,货物的装卸顺序要求对于优化仓门分配和货车排序问题起着重要作用。针对这种情况,以最小化越库操作完工时间为目标,建立越库调度模型。分别基于优化仓门分配和货车排序问题,设计惯性权重非线性改变和增加交叉操作的改进粒子群算法进行迭代寻优。最后通过不同规模的数值实验,将改进粒子群算法与标准粒子群算法和遗传算法进行对比分析,实验结果表明改进粒子群算法在求解精度上比标准粒子群算法和遗传算法有明显优势,在求解时间上优于遗传算法,略逊色于标准粒子群算法。

In a cross-docking system with a wide variety and small batches of cargos, the practical require- ment of cargos＇ unloading/loading sequences plays an important role in the optimization of dock assignment and truck sequence. Under this situation, a new scheduling model to minimize the makespan of cross-docking operations is proposed, and an improved particle swarm algorithm for the proposed model is designed to optimize the dock assignment and the truck sequence. Many numerical experiments are conducted on instances with different scales by using genetic algorithm, standard particle swarm algorithm and improved particle swarm algorithm. Computational results of the three algorithms are compared and it is shown that the proposed algorithm performs better in solution accuracy than standard particle swarm algorithm and genetic algorithm. Its computing time is shorter than that of genetic algorithm and slightly longer than that of standard particle swarm algorithm.