利用深度强化学习解决车辆路径问题：工业卡车规划案例研究
文章背景与核心概要
本文探讨了深度强化学习（DRL）在物流与供应链行业车辆路径问题（VRP）中的应用。尽管VRP是一个经典的运筹学优化难题，但在实际工业场景中，复杂的约束条件、信息不透明以及环境的不确定性往往阻碍了传统算法的有效落地。
作者通过三个具体的外部卡车网络设计物流案例，展示了基于DRL的智能体如何有效优化路径。研究结果表明，与基准方法相比，经DRL优化的路径在总成本上实现了超过10%的显著降低。本文最后呼吁进一步推广DRL算法，以应对更多样化的VRP变体场景。
车辆路径问题与深度强化学习：工业卡车规划案例研究
作者： Siliang Lu, Dan Hu, Lili Wu
日期： 2026年8月7日
学科： 人工智能 (cs.AI)
DOI： 10.48550/arXiv.2608.06668
摘要
作为供应链行业的重要组成部分，运输业在过去十年中借助数字平台和智能算法得到了飞速发展。在交通运输研究领域，车辆路径问题（VRP）始终是一个持久且艰巨的挑战。
As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with the assistance of digital platforms and intelligent algorithms. Within the field of transportation research, the Vehicle Routing Problem (VRP) has remained a persistent and enduring challenge.
在管理科学领域，来自工业界和学术界的专家学者不断探索各种优化模型和算法，以有效解决从经典的旅行商问题（TSP）到更通用的车辆路径问题（VRP）等各类路径规划难题。这些模型和算法被应用于现实工业场景中，旨在实现成本优化并减少碳足迹。
In the realm of management science, experts and scholars from both the industrial and academic sectors have continuously explored optimization models and algorithms to effectively address routing problems, from the classical Traveling Salesman Problem to the more general Vehicle Routing Problem. These models and algorithms are applied in real-world industrial scenarios to achieve cost optimization and reduce carbon footprints.
然而，由于现实世界问题的复杂性，往往会增加许多特定的约束条件，且可能出现信息不透明、不确定性以及非理性人类行为等挑战。因此，在实际场景中部署和优化VRP数学模型并保持最优结果面临诸多困难。本文讨论并提供了针对三种不同外部卡车网络设计物流用例的解决方案。通过这些工业案例研究，本文介绍了基于深度强化学习的车辆路径优化是如何实现的。结果表明，与基准结果相比，由强化学习智能体优化的路径在总成本上实现了超过10%的改善。此外，本文提出在未来的研究中，可以将用于车辆路径问题的DRL算法推广到更多VRP变体中。
However, due to the complexity of real-world problems, numerous specific constraints are often added, and challenges such as information opacity, uncertainty, and irrational human behavior may arise. Therefore, deploying and optimizing mathematical models for VRP in practical scenarios while maintaining optimal results poses numerous challenges. This paper discusses and provides solutions for three different logistic use cases involving external truck network design. Through these industrial case studies, the paper introduces how deep reinforcement learning-based vehicle routing optimization has been implemented. As a result, it can be observed that the routes optimized by the reinforcement learning agent have over 10% total cost improvement compared to baseline results. Furthermore, the paper proposes that in future research, DRL algorithms for vehicle routing problems could be generalized into more variations of VRP.
