A data-driven decision support system for service completion prediction in last mile logistics

The growing demand for last mile services (deliveries and pickups) often results in the work overload of couriers, who are unable to complete all their assigned services within their working day. Uncompleted services are a source of strong dissatisfaction by customers, particularly since they were probably aware that their requested service was scheduled for the day. The possibility of predicting how many and which are going to be these uncompleted services becomes an effective decision-making tool that would allow carriers to increase their perceived service levels without increasing the number of couriers and vehicles. This issue is addressed through the combination of two models. Firstly, machine learning techniques are applied to estimate how many services will remain uncompleted on a given route. Secondly, the use of clustering techniques is proposed as the basis to predict the routes to be followed by couriers, thus identifying potentially uncompleted services as the last ones in each route. The posited methodology is illustrated with a case study comprising four regions in Spain, obtaining promising results in terms of the predictive capacity and the accuracy of the models.

• Record URL:
  • https://doi.org/10.1016/j.tra.2023.103817
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0965856423002379
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Pegado-Bardayo, Ana
  • 0000-0002-5087-203X
  • Lorenzo-Espejo, Antonio
  • 0000-0002-0404-5594
  • Muñuzuri, Jesús
  • Aparicio-Ruiz, Pablo
  • 0000-0002-9979-4183
• Publication Date: 2023-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 103817
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 176
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Decision making; Electronic commerce; First mile and last mile; Machine learning; Pickup and delivery service; Routes and routing
• Geographic Terms: Spain
• Subject Areas: Data and Information Technology; Economics; Freight Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01902763
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 20 2023 3:43PM