Intelligent Courier Delivery Scheduling Based on XGBoost Feature Selection and Reinforcement Learning: A Case Study Using Chongqing Cainiao Express Data

This study tackles the issue of order delays in logistics using XGBoost for feature analysis and reinforcement learning for intelligent courier scheduling. Pickup order data from May 1 to October 31, 2023, in Chongqing is analyzed using spatio-temporal statistical methods. Key findings include that order placement peaks at 9:00 a.m., delays peak at 10:00 a.m., and the delay rate is 8.6%. A significant imbalance exists between the regional daily average of dispatchable couriers and order volumes.XGBoost is employed to predict order delays, revealing that pickup location is the most influential factor (27%), followed by courier pickup location (22%). These factors and their relationships are identified as key drivers of delays.To address these issues, a reinforcement learning-based courier scheduling optimization model is developed. The model defines courier location, current time, and pending orders as state variables and adopts an epsilon-greedy strategy for action selection. Historical data is used to calculate average courier speeds and expected arrival times, updating Q-values based on delays and rewards. Model training results show convergence of rewards and a delay rate reduction from 8.6% to 0.1%. This study highlights the effectiveness of integrating XGBoost with reinforcement learning to reduce delays and improve logistics efficiency. It provides valuable insights into optimizing courier scheduling and addressing the imbalance in regional resources, contributing to smarter and more reliable logistics systems.