基于预测需求量的物流仓储中心选址研究

Research on the Location of Logistics Warehousing Center Based on Demand Forecast

为了解决目前物流仓储中心选址模型存在的脱离实际、需求不合理、成本模型不完备等问题。以长三角地区为例,采用二次指数平滑法预测出该地区27个中心城市未来3年的货物需求量,构建长三角地区物流仓储中心选址模型,利用K-means聚类算法和遗传算法进行仿真实验,并与多种算法进行对比分析。仿真实验表明,K-means聚类算法在求解备选仓储中心问题上有很好的效果,可以使仓储中心到需求点的总距离和最小,遗传算法相比于其他算法在求解仓储中心选址问题上求解速度更快、迭代次数更少、最终结果更精确。结果证明,使用K-means聚类算法和遗传算法求解长三角地区物流仓储中心可以大大提高该地区物流效率并降低物流成本。

In order to solve such problems as impractical location model,unreasonable demand,imcomplete cost model of current logistics warehousing centers,taking the Yangtze River Delta as an example,the quadratic exponential smoothing method is used to predict the demand for goods in 27 central cities in the region in the next three years,and the location model of logistics and warehousing centers in the Yangtze River Delta is constructed,and the K-means clustering algorithm and genetic algorithm are used to simulate experiments,and a variety of algorithms are compared and analyzed.The simulation experiments show that the K-means clustering algorithm has a good effect in solving the problem of alternative storage centers,which can minimize the total distance from the storage center to the demand point.Compared with other algorithms,genetic algorithm has faster solving speed,fewer iterations,and more accurate final results in solving the problem of warehouse center location.The results show that using K-means clustering algorithm and genetic algorithm to solve the logistics warehousing center in the Yangtze River Delta can greatly improve the logistics efficiency and reduce the logistics cost in the region.