Data-Driven Optimization for Meal Delivery: A Reinforcement Learning Approach for Order-Courier Assignment and Routing at Meituan
针对美团外卖配送，提出一种融合强化学习与超启发式优化的混合框架，动态分配订单和规划骑手路径，在真实数据模拟中实现12%的成本降低，并发现高峰期增加骑手比算法改进更有效。
The rapid growth of online meal delivery has introduced complex logistical challenges, where platforms must dynamically assign orders to couriers while accounting for demand uncertainty, courier autonomy, and service efficiency. Traditional dispatching methods, often focused on short-term cost minimization, fail to capture the long-term implications of assignment decisions on system-wide performance. This paper presents a novel hybrid framework that integrates reinforcement learning with hyper-heuristic optimization to improve sequential order assignment and routing decisions in meal delivery operations. Our approach combines n-step state-action-reward-state-action with value function approximation and a multiarmed bandit-based hyper-heuristic incorporating seven specialized low-level heuristics. Our approach explicitly models the evolving system state, enabling dispatching policies that balance immediate efficiency with future operational performance. By employing scalable linear value function approximation, we enhance policy learning in high-dimensional environments while maintaining generalization across states and actions. Using real operational data from the food delivery platform Meituan, we develop a comprehensive simulation environment that captures order dynamics, courier behavior, and service times. Through extensive computational experiments, we demonstrate that our framework significantly outperforms traditional benchmark policies, achieving 12% cost reduction through strategic order postponement. Our results reveal that the largest improvements occur during high-demand periods with courier shortages and that a 10% increase in courier availability yields greater benefits than algorithmic improvements alone. The proposed methodology effectively balances immediate operational efficiency with long-term performance while providing valuable insights for meal delivery platforms regarding courier fleet management and order assignment strategies. History: This paper has been accepted for the Transportation Science Special Issue The First INFORMS TSL Data-Driven Research Challenge.