Same-Day Delivery (SDD) services aim to maximize the fulfillment of online
orders while minimizing delivery delays but are beset by operational
uncertainties such as those in order volumes and courier planning. Our work
aims to enhance the operational efficiency of SDD by focusing on the ultra-fast
Order Dispatching Problem (ODP), which involves matching and dispatching orders
to couriers within a centralized warehouse setting, and completing the delivery
within a strict timeline (e.g., within minutes). We introduce important
extensions to ultra-fast ODP such as order batching and explicit courier
assignments to provide a more realistic representation of dispatching
operations and improve delivery efficiency. As a solution method, we primarily
focus on NeurADP, a methodology that combines Approximate Dynamic Programming
(ADP) and Deep Reinforcement Learning (DRL), and our work constitutes the first
application of NeurADP outside of the ride-pool matching problem. NeurADP is
particularly suitable for ultra-fast ODP as it addresses complex one-to-many
matching and routing intricacies through a neural network-based VFA that
captures high-dimensional problem dynamics without requiring manual feature
engineering as in generic ADP methods. We test our proposed approach using four
distinct realistic datasets tailored for ODP and compare the performance of
NeurADP against myopic and DRL baselines by also making use of non-trivial
bounds to assess the quality of the policies. Our numerical results indicate
that the inclusion of order batching and courier queues enhances the efficiency
of delivery operations and that NeurADP significantly outperforms other
methods. Detailed sensitivity analysis with important parameters confirms the
robustness of NeurADP under different scenarios, including variations in
courier numbers, spatial setup, vehicle capacity, and permitted delay time.

同日配送服务通过在严格的时间限制内将订单匹配和调度给快递员，从而提高配送效率。采用了 NeurADP 方法来解决复杂的一对多匹配和路径规划问题，结果表明 Order batching 和 courier queues 的引入提升了配送效能，而 NeurADP 方法在各种不同情景下都具有稳健性。