基于双重指针网络的车货匹配双重序列决策研究

Study on Dual Sequence Decision-making for Trucks and Cargo Matching Based on Dual Pointer Network

由于我国对公路运输资源利用不均,车货供需问题成为如今的热点问题。车货供需匹配平台为最大化总体运力资源利用率,需要整合运输需求和运力,降低成本并提高效率。大部分平台通常采用启发式算法求解车货匹配问题,此类算法面对大规模的问题时存在寻优瓶颈。针对上述问题,首次将车货供需匹配问题转变成一种双重序列决策问题,据此研究适用于当今车货供需匹配环节的一种高效算法。首先,提出了一种车货匹配的数学模型,并将该模型抽象为双重序列决策问题,再创新性地提出双重指针网络算法求解该问题。本实验使用Actor-Critic算法作为模型的训练框架来训练双重指针网络,并评估了模型。经实验得,双重指针网络的车货匹配求解方法的寻优能力在小问题规模中与传统启发式算法相当,在大问题规模中超越启发式算法,同时时间消耗都大大下降。

Due to the uneven utilization of road transportation resources in my country,the supply and demand of trucks and cargo become a hot issue today.In order to maximize the utilization of overall transportation resources,the truck and cargo supply-demand matching platform needs to integrate transportation demand and capacity,reduce costs and improve efficiency.The algorithms used by most platforms are usually heuristic algorithms to solve the problem of trucks-cargo matching.Such algorithms have a bottleneck in optimizing when faced with large-scale problems.In response to the above-mentioned problems,this paper transforms the supply-demand matching problem of vehicles and goods into a double sequence decision-making problem for the first time.Based on this,we study an efficient algorithm that is suitable for today’s vehicle and goods supply-demand matching links.First,a mathematical model of trucks-cargo matching is proposed and the model is abstracted as a double sequence decision problem,and then a dual-pointer-network algorithm is innovatively proposed to solve this problem.The experiment uses the Actor-Critic algorithm as the model training framework to train the dual-pointer-network and evaluates the model.Experiments show that the dual-pointer-network’s vehicle-to-cargo matching solution method is equivalent to traditional heuristic algorithms in small problem scales,and surpasses heuristic algorithms in large problem scales.At the same time,the time consumption is greatly reduced.