用深度强化学习优化货车路线，工业实测成本降超10%。
Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
- 基于深度强化学习设计货车路径规划算法
- 实测路线成本比基线降低10%以上
- 适合物流优化与智能调度研究者参考
作为供应链行业的重要组成部分，运输在过去十年中在数字平台和智能算法的助力下快速发展。在运输研究领域，车辆路径问题（VRP）始终是一个持久且复杂的挑战。从经典的旅行商问题到更普遍的车辆路径问题，管理科学领域的专家学者持续探索优化模型与算法，以应对实际工业场景中的路线规划，实现成本优化并减少碳足迹。然而，由于现实问题的复杂性，常需加入大量具体约束，信息不透明、不确定性以及人为行为不合理等因素也带来挑战。因此，在保持最优结果的前提下部署和优化数学模型于实际场景面临诸多困难。本文通过三个外部卡车网络设计的工业案例，探讨并提供了深度强化学习（DRL）在车辆路径优化中的应用方案。结果显示，强化学习代理生成的路线总成本较基线结果降低超过10%。此外，论文提出未来可将此类DRL算法推广至更多变体的VRP问题中。
原文摘要 · Abstract (English)
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.
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