关键词: 公路运输, 货运平台, 量子粒子群算法, 城际零担运输, 三维装载, 车辆路径

Abstract: Under the subsidy schemes of intercity transportation platforms for less-than-truck-load cargo, we established a vehicle routing optimization model considering goods-vehicles matching and three-dimensional loading constraints. The model minimized the sum of platform subsidy cost, vehicle operation cost, and fuel cost. To solve this model, we designed a hybrid quantum particle swarm optimization algorithm to determine the optimal cargo matching, vehicle path, cargo loading and unloading, and platform subsidy. The experimental results show that the gap between the solution obtained by the hybrid quantum particle swarm optimization algorithm and the optimal solution obtained by CPLEX software is 3.31% on average in small-scale cases. By introducing the fitness function value as the weight in solving the optimal middle position, the solution in the large-scale examples is 0.91% higher than the traditional quantum particle swarm optimization algorithm. By analyzing the characteristics of the optimal solution, the improved hybrid quantum particle swarm optimization algorithm is combined with a heuristic algorithm, and the solution quality is improved by 4.05% . Through the comparative experiments of subsidy modes, it is found that in a reasonable planning cycle, the increase of cargo owner time subsidy and no-load subsidy can effectively improve the platform profit and vehicle utilization while maintaining the total cost basically unchanged.

Key words: highway transportation, freight platform, quantum particle swarm optimization, intercity less-thantruckload cargo transportation, three-dimensional loading, vehicle routing