Beyond Distance: Learning Courier Behavior with Large Language Models

Last-mile delivery is a cornerstone of modern urban logistics, and its efficiency depends heavily on high-quality route planning. Conventional approaches typically optimize for a single geometric objective such as minimizing travel distance or time, as in the classical Traveling Salesman Problem (TSP). Solving such problems to optimality can require substantial computation time as the number of delivery stops increases. However, these methods overlook practical factors that shape real-world delivery, including couriers' behavioral preferences. To address these limitations, we propose the Delivery Optimization and Route Advisor (DORA), a route-planning system built on the compact Qwen3-4B-Instruct model and an adapted Group Relative Policy Optimization (GRPO) algorithm. DORA learns couriers' behavioral patterns from real delivery traces and recommends routes that better reflect couriers’ observed delivery patterns. We train and evaluate DORA on LaDe, a publicly available last-mile delivery dataset, and on the Amazon Last Mile Routing Research Challenge (ALMRRC) as well. DORA is designed to reduce inference costs relative to larger language-model-based approaches, supporting cost-effective deployment for last-mile route prediction.