A scalable anticipatory policy for the dynamic pickup and delivery problem
Dynamic vehicle dispatching and routing problems can be tackled by using either reactive policies (that optimize the overall inconvenience on the pending requests) or anticipatory policies (that consider the possible future demands). The anticipatory policies reported in the literature are typically unsuitable for the large instances often encountered in the real-world, where the inter-arrival time can be as little as a few seconds. In this article, the authors present a new scalable anticipatory policy for the Dynamic Pickup and Delivery Problem which amounts to design routes for a fleet of vehicles that must service a set of pickup and delivery requests, characterized by different priority classes, arriving according to an unknown (possibly time-varying) stochastic process. The algorithm utilizes a parametric policy function approximation in which the best parameter setting is chosen on-line on the basis of a mapping between instance features and policy parameters learned off-line by using simulation experiments. Computational results on large-scale randomly-generated instances indicate that the authors' anticipatory procedure outperforms two reactive approaches while keeping the computational burden at a level suitable for real-world usage.
- Record URL:
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/1793974
-
Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
-
Authors:
- Ghiani, Gianpaolo
- Manni, Andrea
-
0000-0001-5716-5824
- Manni, Emanuele
-
0000-0002-2909-1386
- Publication Date: 2022-11
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 105943
-
Serial:
- Computers & Operations Research
- Volume: 147
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0305-0548
- Serial URL: https://www.sciencedirect.com/journal/computers-and-operations-research
Subject/Index Terms
- TRT Terms: Forecasting; Machine learning; Pickup and delivery service; Routing
- Subject Areas: Freight Transportation; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01858100
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 20 2022 2:33PM