Order Allocating Optimization For Multi-shuttle Picking System

The development of electronic commerce has driven the development of logistics. So the efficient, fast, personalized logistics service becomes the most important reason to electricity supplier logistics company to obtain the rapid development. According to the reason above, many logistics companies choose a breakthrough in the technology and the multi-layer shuttle warehouse sorting system is one of their new technology. This kind of picking system can improve the sorting efficiency, improve accuracy, reduce labor cost and save the storage space, which have broad prospects for development. In this context, how to improve the efficiency of multi-shuttle sorting and storage system has become an important problem that the logistics planners need to consider. Now there are rare papers about the order allocating in goods-to-people picking system and the order allocating strategy in multi-shuttle picking system has never been researched neither. So it’s indeed needed to study about the order allocating strategy in multi-shuttle picking system based on the theory about goods-to-people picking system.This paper introduced the multi-layer shuttle picking system at first and gave an analysis about the procedure of inbound and outbound, picking and sorting. Taking the reduce of the box inbound and outbound as a standard to measure the optimization effect, the paper explained the influence of the picking efficiency by the different order allocating strategy. The paper introduced a non-clustering order allocating strategy, and built a mathematical model to get a result that the reasonable order allocating can improve the picking efficiency. Then this paper raised four strategies based on improved k-means clustering algorithm. At last the paper gave a simulation result by the true orders of a logistics company and had an analysis to the results to get the effects of the different strategy.According to the analysis, the optimization efficiency results from best to better are:order allocation strategy based on changed similar matrix, order allocation strategy based on fixed similar matrix, maximum goods items in orders as clustering reference order allocation strategy, the random order as clustering reference order allocation strategy, the non clustering order allocation strategy. Because of the limitation of the computer processing ability, the researchers chose the maximum goods items in orders as clustering reference order allocation strategy or the random order as clustering reference order allocation strategy as the using strategy in practice while the top two cost too much time.