Abstract
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.
Highlights
With the development of Internet technology, a series of new solutions and adjustments are designed to the layout of the logistics industry
vehicle-commodity matching problem (VCMP) is proposed to upgrade the service of logistics providers by matching vehicles and commodities. is is different from the global perspective of logistics system optimization; our research focuses on service details of matching
Gaussian mixture model (GMM)-EM algorithm is used to match vehicles and commodities according to time window, distribution distance, and path characteristics
Summary
With the development of Internet technology, a series of new solutions and adjustments are designed to the layout of the logistics industry. VCMP is proposed to upgrade the service of logistics providers by matching vehicles and commodities. Mafakheri et al [15] proposed a two-stage dynamic planning method for supply chain management to solve the problem of multisupplier ranking and introduced the supplier parameters into an order allocation model to maximize the utility of the company. Is paper aims at the cost optimization of the whole logistics operation process of e-commerce enterprises from order allocation to order delivery. 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 vehiclecommodity matching. The matching process of VCMP is divided into four parts, including customers, orders, vehicles, and warehouses. Objective Function. e total cost (TC) of the distribution process includes three parts: fixed cost of distribution warehouse, fixed cost of freight vehicles, and variable cost of transportation process. e objective function of the integration problem is as follows: min TC bpxp + Hqyqij + Wqij. (1)
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