English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Same-Day Delivery (SDD) services aim to maximize the fulfillment of onlineorders while minimizing delivery delays but are beset by operationaluncertainties such as those in order volumes and courier planning. Our workaims to enhance the operational efficiency of SDD by focusing on the ultra-fastOrder Dispatching Problem (ODP), which involves matching and dispatching ordersto couriers within a centralized warehouse setting, and completing the deliverywithin a strict timeline (e.g., within minutes). We introduce importantextensions to ultra-fast ODP such as order batching and explicit courierassignments to provide a more realistic representation of dispatchingoperations and improve delivery efficiency. As a solution method, we primarilyfocus on NeurADP, a methodology that combines Approximate Dynamic Programming(ADP) and Deep Reinforcement Learning (DRL), and our work constitutes the firstapplication of NeurADP outside of the ride-pool matching problem. NeurADP isparticularly suitable for ultra-fast ODP as it addresses complex one-to-manymatching and routing intricacies through a neural network-based VFA thatcaptures high-dimensional problem dynamics without requiring manual featureengineering as in generic ADP methods. We test our proposed approach using fourdistinct realistic datasets tailored for ODP and compare the performance ofNeurADP against myopic and DRL baselines by also making use of non-trivialbounds to assess the quality of the policies. Our numerical results indicatethat the inclusion of order batching and courier queues enhances the efficiencyof delivery operations and that NeurADP significantly outperforms othermethods. Detailed sensitivity analysis with important parameters confirms therobustness of NeurADP under different scenarios, including variations incourier numbers, spatial setup, vehicle capacity, and permitted delay time.

Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem