需求变动下高铁快运货流分配与组织模式优化

基于高铁快运一地集货和多点发运的开行特点，综合考虑我国未来快递市场的需求变动，提出高铁快运常态化和规模化成网运营后的货流分配与组织模式优化方法，以客运动车组捎带运输货物、预留车厢和高铁快运专列模式下，各OD对间的高铁快运运量为主要决策变量，以总运输成本极小化为目标，构建货流分配混合整数规划模型，计算货流量在高铁快运网络中的分布，得到货流OD运输路线与各高铁快运通道运营组织模式的组合优化方案。通过高铁快运货流OD “三阶段”预测方法，预测城市间的高铁快运需求量作为货流输入，以9个国内快递业务中心城市组成的网络为案例，由Python语言调用Gurobi优化软件求解案例，验证所建模型的有效性。案例结果表明：在运量低谷期使用捎带和预留模式可满足90.7%的运输需求；在运量高峰期部分线路需开行高铁快运专列；运营组织模式可根据OD对间货流量与网络容量的适应性关系进行调整，以充分利用高铁闲置运力，提升运营效益。

High-speed Railway Express Cargo Flow Allocation and Operation Organization Optimization Under Varying Demand

This paper proposes the optimization method for cargo flow allocation and organization under the normalization and large-scale network operation for the high-speed railway (HSR) express considering the operational characteristics of the HSR express with one place cargo collection and multiple places for shipment and the variation of the demand of China's express market in the future. The main decision variables are the volume of HSR express between each Origin Destination (OD) pair under the mode of passenger train piggyback transport, reserved carriage and HSR freight train. The objective is to minimize the total operating costs. A mixed- integer programming (MIP) model is proposed for cargo flow allocation, which calculates the allocation of cargo flow in the HSR express network and creates the combination optimization scheme of cargo flow OD transport route and the operation organization mode for each HSR express channel. The method of "three-stage" forecasting of HSR express cargo flow OD is used to forecast the demand for HSR express between cities as the cargo flow input. The numerical experiments based on a network consisting of nine domestic express central cities are conducted to verify the effectiveness of the proposed methods, which are all computed by the Gurobi solver code in the Python programming language. The results show that using the piggyback and reserved modes in the low volume period can accommodate 90.7% of the transport needs. In the peak period, the operation organization mode can be adjusted according to the adaptive relationship between OD pairs of cargo flow and network capacity so as to fully utilize the idle capacity of HSR and improve operational efficiency.