车货匹配问题:基于货运数据统计特征研究

On the Vehicle-Cargos Matching Problem Based on Statistical Characteristics of Freight Data

公路货运是物流运输行业中的重要组成部分,我国已成为世界第一大公路运输市场,但物流效率不高。基于此,本文利用在线货运交易平台的历史货运大数据,挖掘分析货车运输距离分布规律、运输经济距离等特征,将其融入基于数据驱动的优化模型,构建在线货运交易平台视角的区域公路货运网络运输成本、时间成本和碳税成本的综合成本最优的车货匹配模型,并设计算法求解算例模型,以探究在实现总体成本最优下的货运订单分派策略。结果表明综合公路货运网络决策模型比仅单纯考虑运输成本的订单分配模型为平台节省了3%的综合成本,有效提升了企业的成本效益和环境效益。

Road freight is an important part of the logistics transportation industry. China has become the largest kilometer transportation market in the world, but the logistics efficiency is low. Based on it, this paper used freight big data to mine and analyze the characteristics of road freight including distribution law of truck transportation distance and the transportation economic distance. Then, this paper established an optimal freight order dispatching model for the comprehensive cost of transportation cost, time cost and carbon tax cost from the perspective of the online freight transaction platform, integrating these truck freight characteristics into it. Finally, this paper designed algorithm to solve the model to explore the freight order distribution strategy under the optimal overall cost, the results show that the comprehensive decision-making model established in this paper saves 3% of the comprehensive cost for the platform compared with the one which only considers transportation costs, and effectively improves the cost-effectiveness and environmental benefits of the enterprise.