Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
作者: Siliang Lu, Dan Hu, Lili Wu
分类: cs.AI
发布日期: 2026-08-07
💡 一句话要点
基于深度强化学习的车辆路径优化解决物流调度问题
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)
关键词: 车辆路径问题 深度强化学习 物流优化 供应链管理 智能调度 成本降低 算法应用
📋 核心要点
- 车辆路径问题（VRP）在实际应用中面临复杂性和多种约束，现有优化方法难以有效应对。
- 本文提出利用深度强化学习（DRL）算法进行车辆路径优化，旨在提高调度效率和降低成本。
- 实验结果显示，基于DRL的优化方案相比传统基线方法，能够实现超过10%的成本降低。
📝 摘要（中文）
运输作为供应链行业的重要组成部分，在过去十年中随着数字平台和智能算法的助力迅速发展。车辆路径问题（VRP）在运输研究中始终是一个持久的挑战。本文探讨了在实际工业场景中，如何通过深度强化学习优化车辆路径，以实现成本优化和减少碳足迹。通过三个不同的物流用例，研究表明，强化学习优化的路径相比基线结果降低了超过10%的总成本，并提出未来研究可将深度强化学习算法推广到更多VRP变种中。
🔬 方法详解
问题定义：本文旨在解决车辆路径问题（VRP），现有方法在面对复杂约束和不确定性时，往往难以保持优化效果。
核心思路：通过引入深度强化学习算法，利用智能体学习最优路径，克服传统方法在动态环境中的局限性。
技术框架：整体架构包括数据收集、模型训练和路径优化三个主要模块。数据收集阶段获取实时物流信息，模型训练阶段使用强化学习算法进行路径优化，最后进行实际调度。
关键创新：论文的主要创新在于将深度强化学习应用于车辆路径优化，显著提高了在复杂约束下的调度效率，与传统优化方法相比具有更强的适应性。
关键设计：在模型设计中，采用了特定的损失函数来平衡成本和时间，网络结构则基于深度Q网络（DQN），并进行了超参数调优以提高学习效果。
🖼️ 关键图片
📊 实验亮点
实验结果表明，基于深度强化学习的路径优化方案相比传统基线方法，能够实现超过10%的成本降低。这一显著提升展示了深度学习在复杂调度问题中的应用潜力。
🎯 应用场景
该研究的潜在应用领域包括物流运输、供应链管理和智能交通系统。通过优化车辆路径，可以显著降低运输成本和碳排放，提升整体运营效率，具有重要的实际价值和社会影响。
📄 摘要（原文）
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, Vehicle Routing Problem (VRP) has remained a persistent and enduring challenge. 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. 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 study, 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 reinforcement learning agent have over 10% total cost 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.