Deep Reinforcement Learning for the Capacitated Pickup and Delivery Problem with Time Windows

The vehicle routing problem with pickup and delivery is one of the most important problems in the context of global urban population growth. Although these kinds of small-size problems can be solved using various classical approaches, a fast (or real-time) route optimizer under real-world constraint...

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Bibliographic Details
Published in:Pattern recognition and image analysis 2023-06, Vol.33 (2), p.169-178
Main Authors: Soroka, A. G., Meshcheryakov, A. V., Gerasimov, S. V.
Format: Article
Language:English
Subjects:
Computer Science
Deep learning
Deliveries
Image Processing and Computer Vision
Pattern Recognition
Population growth
Problems
Reinforcement
Selected Conference Papers
Vehicle routing
Windows (intervals)
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Summary:The vehicle routing problem with pickup and delivery is one of the most important problems in the context of global urban population growth. Although these kinds of small-size problems can be solved using various classical approaches, a fast (or real-time) route optimizer under real-world constraints (such as throughput and time window constraints) for medium- and large-size problems is still a challenge. In this work, we first successfully applied a deep reinforcement learning approach (a modified JAMPR model) to solve the capacitated pickup and delivery problem with time windows (CPDPTW). We obtained a robust model that gives a fast optimal solution for small- to medium-size problems and gives a fast suboptimal solution for large-size (>200) problems.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661823020165