自动化立体仓库货位分配与作业调度集成优化

针对动态提高单载具堆垛机式自动化立体仓库拣选效率的问题,文中提出了一种基于共享货位存储与动态订单拣选策略下的货位分配与作业调度集成优化方法。将动态移库优化扩展到仓库的整个拣选生命周期,建立以双指令循环下堆垛机拣选任务所需的总作业时间最短为评价目标的数学模型,提出了一种基于K-Medoids聚类的粒子群优化(Particle Swarm Optimization,PSO)算法,用K-Medoids算法通过产品与订单的相关性进行初始货位的聚类分析,筛除劣质解的货位范围,并在K-Medoids聚类算法生成的解类簇基础上获得精确解。实验结果表明,考虑动态移库可以使仓库拣选效率提高20%,且该算法与传统PSO算法相比求解时间下降66%左右。

Integrated Optimization of Location Assignment and Job Scheduling in Automated Storage and Retrieval System

To improve the dynamic operation efficiency of single shuttle stacker Automated Storage and Retrieval System(AS/RS),the integrated optimization method of location assignment and job scheduling based on shared location storage and dynamic order picking strategy is proposed.The dynamic shift library optimization is extended to the entire picking life cycle of the warehouse,the mathematical model with minimized total working time required for the stacker to do tasks under single shuttle dual-command cycle is established.The PSO algorithm based on K-Medoids clustering algorithm is designed,K-Medoids algorithm is used to analyze the initial location of the product through the correlation between the product and the order,screen out the range of inferior quality solutions,and the PSO algorithm is used to find the exact solution to the problem based on the class cluster of the solution generated by the K-Medoids class algorithm.The experiments show that considering the transfer case under special circumstances could really improve 20%of the operation efficiency of the warehouse and the solution time of the algorithm could reduce about 66%compare with the traditional PSO algorithm.