Optimization Research Of Parallel Multi-Orders Picking Model

The order-picking is an important part of logistics scheduling. The usage of automatic equipment improves sorting efficiency instead of a large amount of traditional human labor. However, the expense of automatic equipment purchase and maintenance costs are high, if there is no good means of scheduling, it is difficult to demonstrate its economic benefits. The study of manufacturing industry focus on the rational allocation of automatic equipment, comprehensively utilization of different equipment and ways of sorting, improvement of sorting efficiency and reduction of sorting costs.In the process of multi-order parallel sorting optimization, we mainly consider the inherent relationship among the various orders to achieve parallelism. The associated items are mainly contained the similarity of the items among the orders and storage relevance among the items. Fastener industry is a multi-species, high-volume, large changes in demand and a shorter lead time industry. The EIQ methods can analyze the characteristics of the order to provide for the selection of reference for strategy development. Through the analysis of actual data, it shows that the combined use of EIQ and ABC classification method can effectively select the center of the plan to make a program which was to adapt the sorting order needs to improve sorting efficiency and equipment utilization, with a good economic efficiency and expansion .Considering the inherent correlation of the multi-order, parallel sorting process is a two-stage process. First, we should consider batches of the orders. Second, the after-batching orders sorted by the automatic sorting equipment for parallel sorting. In this thesis, through the actual investigation of a logistics workshop, combining with the actual order data of enterprise, when we design the order in batches, we should consider the batch strategy of order picking in the same car. I put forward the use of Genetic Algorithm and discuss the parameters set. In the second stage, based on the practical constraints of the automatic three-dimensional warehouse systems, I proposed an improved ant colony algorithm for optimal solution of the request stacking machine scheduling problem.