Abstract:

In logistics supply chain management， the warehouse as the storage place of goods， its storage optimization has been the focus of research by warehouse managers and researchers. The allocation of storage positions and goods picking are two important aspects of inventory management. If the space allocation strategy is appropriate， the efficiency of goods picking can be effectively improved. The ABC allocation strategy commonly used in warehouse storage classifies goods by category and turnover. This allocation method does not consider the same kind of goods for split placement. However， when demand is uncertain， considering the need of safe stock， the selection of a few items in the replenishment cycle of a single goods is a low probability event. The ABC allocation strategy will make the items with a low probability of being selected in the goods with a high total turnover frequency also occupy the position closer to I/O point. The low turnover frequency leads to an increase in total picking distance and a decrease in picking efficiency.In this paper， a new allocation strategy based on uncertain demand is proposed. By considering the different demand probability of different quantity items in a single goods， the strategy divides the same goods into different areas. The low-probability demand portion of the goods will be stored away from the I/O point. In this way， the total demand probability near the I/O point is increased， thus making the storage and picking process more efficient. For the convenience of description and understanding， the goods location allocation strategy proposed in this paper is defined as the Probability-of-Retrieval（POR） goods location allocation strategy. The ABC location allocation strategy sorts goods according to turnover rate， but the POR strategy allocates the same goods according to the different demand probability under the different quantity in the replenishment period， and determines a probability boundary as the partition boundary， all kinds of goods are stored randomly within the same boundary.The average one-way picking distance is taken as the optimization goal. Firstly， the optimal order quantity of each goods is gotten by combining EOQ replenishment strategy. Secondly， according to the assumption of normal distribution and the POR location allocation strategy， the expressions of storage space， weighted turnover frequency and picking distance of goods in different subareas and the expressions of picking distance of ABC location allocation under uncertain demand are derived. Finally， Matlab 2018A is used to carry out numerical experiments， and the optimized results are compared under the different variable parameters such as demand curve， standard deviation， service level and so on， the influence of the change of the probability partition on the total picking distance is discussed.The experimental results show that under different parameters， the POR location allocation strategy is better than the traditional ABC location allocation strategy to varying degrees. The smoother the demand curve is， the smaller the demand difference of various goods is. The model based on demand probability proposed in this paper can save the total travel distance more than the traditional model， but at the same time， the increase of optimization degree is also more and more gentle； Under the POR strategy， too high service level is not appropriate； The smaller the standard deviation of goods demand distribution， the higher the degree of optimization； The more probability zones， the smaller the travel distance. Under the common ABC slope curve of 20% goods contributing 80% demand， in each probability partition strategy， when the cumulative demand probability of goods in the first partition （the area closest to the I/0 point） is about 20%， the total picking distance is relatively shortest.In the actual storage， the warehouse manager can estimate the demand distribution according to the previous orders， get the average value and standard deviation of different kinds of goods， and arrange the goods distribution better under the partition storage allocation strategy based on the demand probability.

Key words: allocation of storage positions, demand uncertainty, partition picking, storage strategy