Z公司随机存储策略下的货位优化研究

发布时间：2018-05-17 04:08

本文选题：B2C电商 + 随机存储　；参考：《北京交通大学》2017年硕士论文

【摘要】：配送速度是衡量B2C电商企业竞争力和服务能力的一个重要指标。在各大电商企业的销售量、营业收入稳步增长的今天,配送速度更是成为各电商企业间竞争的重要指标。在对Z公司及其PEK配送中心的调研中发现,人为因素造成的延误中,有31%是因为配送中心订单货物出库晚造成的。进一步分析可以知道,PEK配送中心是人工拣选配送中心,其采用的是随机存储策略,这种存储策略虽然可以加快货物上架的速度,提高仓储利用率,但由于会增加拣货员的行走路径,而影响到配送中心订单货物的出库时效。货位优化是影响配送中心出库时效的一个关键点,货位能否合理分配直接影响货物能否快速上架,并按照需求被准确拣选、及时出库,进而影响电商企业的整体服务能力。在随机存储策略下,怎样进行货位优化,成为Z公司PEK配送中心提升出库时效的主要问题。本文以人工拣选的PEK配送中心为研究对象,针对PEK配送中心存在的货位分配缺乏合理规划、出库效率较低等问题,分别研究了配送中心初始化状态时的货位优化问题以及已经存有商品时上架商品的货位优化问题。在初始化状态时,考虑商品的出库频次,结合靠近出口原则和商品之间的相关性,将相关性高的商品存储在相邻的货位中,同时,将出库频次高的商品放在靠近出口的巷道中。随着时间的推移,商品之间的相关性不断变化,在已经存有商品的配送中心,模拟商品整个出入库的动态过程,考虑上架商品与整个巷道内商品的动态相关性,商品之间的相关性系数根据最新的历史订单进行计算,将上架商品存储在满足体积约束,并且当前时刻与其相关性最大的巷道中,而在每一个巷道内,商品都是随机存储的。固定存储的货位优化是一劳永逸的,而文本中,每一种商品上架时,都会根据最新的商品相关性实时进行货位优化,这正体现了随机存储的灵活多变。通过缩短拣货员行走的路径,既能够发挥随机存储的优势,又能够提高配送中心的出库时效。
[Abstract]:Distribution speed is an important index to measure the competitiveness and service ability of B-2 C e-commerce enterprises. With the steady growth of sales volume and business income, distribution speed has become an important indicator of competition among e-commerce enterprises. In the investigation of Z Company and its PEK distribution center, it is found that 31% of the delays caused by human factors are due to the late delivery of goods ordered in the distribution center. Through further analysis, we can know that PEK distribution center is a manual picking distribution center, and it adopts a random storage strategy. Although this storage strategy can speed up the loading of goods and increase the utilization rate of storage, However, due to the increase of picker's walking path, it affects the delivery time of delivery center order goods. The optimization of cargo location is a key point that affects the limitation of delivery in distribution center. Whether the goods can be allocated reasonably or not directly affects whether the goods can be put on shelves quickly and be selected accurately according to the demand and leave the warehouse in time, thus affecting the overall service ability of the e-commerce enterprises. Under the random storage strategy, how to optimize the cargo location becomes the main problem of Z company PEK distribution center to enhance the time of delivery. In this paper, the artificially selected PEK distribution center is taken as the research object. The distribution of goods in PEK distribution center is lack of reasonable planning, and the efficiency of delivery is low, and so on. In this paper, the problem of cargo location optimization in the initial state of distribution center and the problem of cargo location optimization when goods are already in stock are studied respectively. In the initialization state, considering the frequency of goods out of storage, combining the close to export principle and the correlation between commodities, the goods with high correlation are stored in the adjacent cargo location, and the goods with high frequency of exit are placed in the roadway near the exit. Over time, the correlation between commodities is constantly changing. In the distribution center where the goods already exist, the dynamic process of goods entering and out of the warehouse is simulated, and the dynamic correlation between the goods on the shelf and the goods in the whole roadway is considered. The correlation coefficient between commodities is calculated according to the latest historical orders, and the goods on the shelves are stored in the roadway which satisfies the volume constraint and is most relevant at the present time. In each laneway, the goods are stored randomly. The fixed-storage cargo location optimization is once and for all, and in the text, when each kind of goods is put on shelves, it will carry on the goods position optimization according to the latest commodity correlation in real time, which reflects the flexibility of random storage. The advantage of random storage and the time limit of delivery center can be improved by shortening the path of sorting staff.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F252;F713.36

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