An End-to-End Predict-Then-Optimize Clustering Method for Stochastic Assignment Problems

Express pickup and delivery systems play crucial roles in contemporary urban areas. Couriers within these systems retrieve packages from designated Areas of Interest (AOI) that the express company assigns to them during specific time intervals. The express company traditionally employs historical pickup request data for executing AOI assignments (or pickup request assignments) for couriers, and these assignments are conventionally static and do not evolve over time However, future pickup requests display significant temporal variations. Employing historical data for future assignments is, therefore, somewhat impractical. Furthermore, even if future pickup requests could be predicted beforehand and subsequently employed for assignments, this two-stage approach proves to be both impractical and trivial, potentially harboring drawbacks. For example, the better prediction results may not necessarily guarantee better clustering outcomes. To address these challenges, the authors introduce an intelligent end-to-end predict-then-optimize clustering method that simultaneously forecasts future pickup requests for AOIs and dynamically allocates AOIs to couriers through clustering. Initially, the authors propose a deep learning-based prediction model for predicting order quantities within AOIs. Subsequently, the authors present a differential constrained K-means clustering method for AOI clustering based on the prediction results. Finally, the authors introduce a one-stage end-to-end predict-then-optimize clustering approach for the rational, dynamic, and intelligent allocation of AOIs to couriers. The results demonstrate that this one-stage predict-then-optimize method significantly enhances optimization outcomes, namely the quality of clustering results. This study offers valuable insights that are relevant to predict-then-optimize-related tasks, particularly when addressing stochastic assignment problems within all types of express systems.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2024.3385029
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Zhang, J
  • Shan, E
  • Wu, Libing
  • 0000-0001-9897-1953
  • Yin, J
  • Yang, Longhao
  • Gao, Z
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 12605-12620
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Logistics; Machine learning; Optimization; Pickup and delivery service; Predictive models; Stochastic processes; Urban areas
• Subject Areas: Freight Transportation; Operations and Traffic Management;

Filing Info

• Accession Number: 01938889
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 6 2024 2:15PM