DECO: Cooperative Order Dispatching for On-Demand Delivery with Real-Time Encounter Detection
In on-demand delivery,online orders are delivered by couriers from merchants to customers within a short time (e.g., 45 minutes). An important task is to provide an efficient order dispatching solution. Existing studies focus on scenarios with stable routing behavior using pre-determined courier-order matching before delivery while ignoring real-time dynamics during delivery. In this work, we leverage courier-courier encounter events as an opportunity to enable cooperative order dispatching (i.e., conducting order transfers among couriers during delivery) for better delivery efficiency. However, it is non-trivial to conduct encounter-aware cooperative order dispatching in real-time dynamics due to two major challenges: (i) the dynamic nature of encounters in diverse real-world scenarios, and (ii) global delivery efficiency optimization by local order transfers. To address the above challenges, we design a detection-driven cooperative dispatching framework, called DECO. Specifically, we design (i) a Received Signal Strength Indicator (RSSI) variance-based state encoder to model encounter dynamics, (ii) an encounter event selector to choose encounter scenarios, (iii) a time-constrained order mask module to filter unsuitable orders, and (iv) an encounter-aware order transfer scheduler to make detailed order transfer decisions. Extensive experiments on real-world data from two large companies (i.e., JD Logistics, Eleme) show that DECO outperforms other baselines.Real-world deployment results at JD Logistics show that DECO improves the order overdue rate by 4.8%.
Authors
- Hai Wang (ORCID: https://orcid.org/0000-0001-7317-507X)
- Tian He (ORCID: https://orcid.org/0000-0001-6062-2619)
- Yu Yang (ORCID: https://orcid.org/0000-0003-1627-5503)
- Shuai Wang (ORCID: https://orcid.org/0000-0003-2766-1135)
- Baoshen Guo (ORCID: https://orcid.org/0000-0002-7435-8238)
- Shuai Wang (ORCID: https://orcid.org/0000-0001-6838-1151)
- Desheng Zhang (ORCID: https://orcid.org/0000-0001-9307-8736)
- Yao Lu (ORCID: https://orcid.org/0000-0002-9706-8612)
Institutions
- Rutgers, The State University of New Jersey (US)
- Lehigh University (US)
- Jingdong (China) (CN)
- Southeast University (CN)
Publication Details
- Published
- 2024-10-20
- DOI
- https://doi.org/10.1145/3627673.3680084
- Citations
- 1
- Primary Topic
- Optimization and Search Problems
- Type
- article
- Field-Weighted Citation Impact
- 0.35