基于深度强化学习的车辆路径问题——工业中卡车规划的案例研究

中文摘要 AI 辅助

作为供应链行业的重要组成部分，运输业在数字平台和智能算法的助力下，过去十年发展迅速。在运输研究领域，车辆路径问题（Vehicle Routing Problem，VRP）始终是一项持久的挑战。在管理科学领域，工业界和学术界的专家学者不断探索优化模型与算法，以有效解决路径规划问题，从经典的旅行商问题到更通用的车辆路径问题，这些模型与算法被应用于实际工业场景，以实现成本优化并减少碳足迹。但由于实际问题的复杂性，常需添加诸多特定约束，还会出现信息不透明、不确定性、人类非理性行为等挑战。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挑战。本文针对涉及外部卡车网络设计的三个不同物流应用场景展开讨论并提供解决方案，通过这些工业案例研究，介绍了基于深度强化学习（Deep Reinforcement Learning，DRL）的车辆路径优化的实施方式。结果表明，强化学习智能体优化后的路径总成本较基线结果降低了10%以上。此外，本文提出未来研究可将用于车辆路径问题的DRL算法推广到更多VRP变体中。

英文摘要

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.

发表机构

• Bosch Center for Artificial Intelligence（博世人工智能中心）
• School of Electronic Information and Electrical Engineering（电子信息与电气工程学院）
• School of Computer Science, Macau University of Science and Technology（澳门科技大学计算机学院）

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