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Expectation-Maximization Algorithm of Gaussian Mixture Model for Vehicle-Commodity Matching in Logistics Supply Chain
Complexity ( IF 2.462 ) Pub Date : 2021-01-13 , DOI: 10.1155/2021/9305890
Qi Sun; Liwen Jiang; Haitao Xu

A vehicle-commodity matching problem (VCMP) is presented for service providers to reduce the cost of the logistics system. The vehicle classification model is built as a Gaussian mixture model (GMM), and the expectation-maximization (EM) algorithm is designed to solve the parameter estimation of GMM. A nonlinear mixed-integer programming model is constructed to minimize the total cost of VCMP. The matching process between vehicle and commodity is realized by GMM-EM, as a preprocessing of the solution. The design of the vehicle-commodity matching platform for VCMP is designed to reduce and eliminate the information asymmetry between supply and demand so that the order allocation can work at the right time and the right place and use the optimal solution of vehicle-commodity matching. Furthermore, the numerical experiment of an e-commerce supply chain proves that a hybrid evolutionary algorithm (HEA) is superior to the traditional method, which provides a decision-making reference for e-commerce VCMP.

中文翻译:

物流供应链中车品匹配的高斯混合模型期望最大化算法

为服务提供商提供了一种汽车商品匹配问题(VCMP),以降低物流系统的成本。将车辆分类模型构建为高斯混合模型(GMM),并设计了期望最大化(EM)算法来求解GMM的参数估计。构造了一个非线性混合整数编程模型以最小化VCMP的总成本。车辆和商品之间的匹配过程由GMM-EM实现,作为解决方案的预处理。用于VCMP的车品匹配平台的设计旨在减少和消除供需之间的信息不对称性,从而使订单分配可以在正确的时间和地点进行,并使用车品匹配的最佳解决方案。此外,
更新日期:2021-01-13
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