Centralized Deep Reinforcement Learning Method for Dynamic Multi-Vehicle Pickup and Delivery Problem with Crowdshippers
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 9253-9267 |
Journal / Publication | IEEE Transactions on Intelligent Transportation Systems |
Volume | 25 |
Issue number | 8 |
Online published | 31 Jan 2024 |
Publication status | Published - Aug 2024 |
Link(s)
Abstract
Crowdshipping problem can be challenging as the platform are continuously but sporadically receiving crowdshippers and delivery tasks with heterogeneous origin and destination. In this paper, the dynamic multi-vehicle pickup and delivery problem with crowdshippers (DMV-PDPC) is considered. Leveraging the deep reinforcement learning framework, the attention model with centralized vehicle network (AMCVN) method is developed. Unlike traditional heuristic or existing vehicle-changing methods, AMCVN integrates a centralized vehicle network (CVN) that can observe the state information of all vehicles, enhancing its overall performance. In each decision-making step, the CVN monitors the state of the vehicles and selects one of the vehicles. Subsequently, the attention-based route generating network (RGN) determines the next node to be visited by the chosen vehicle. Instead of using a penalty term in the reward function to regulate the sequence of visits to pickup and delivery nodes, a more precise control method, namely the rolling mask scheme (RMS), is implemented. The method's evaluation is carried out via a simulation experiment using a real-world road network. This evaluation demonstrates that the proposed method effectively tackles the DMV-PDPC challenge, outperforming current state-of-The-Art learning-based models and heuristic methods. Moreover, the method shows exceptional generalization capabilities, as evidenced by its adaptability to different numbers of tasks and vehicles. © 2024 IEEE..
Research Area(s)
- attention mechanism, Crowdshipping, deep reinforcement learning, dynamic vehicle routing problem
Citation Format(s)
Centralized Deep Reinforcement Learning Method for Dynamic Multi-Vehicle Pickup and Delivery Problem with Crowdshippers. / Xiang, Chuankai; Wu, Zhibin; Tu, Jiancheng et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 8, 08.2024, p. 9253-9267.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 8, 08.2024, p. 9253-9267.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review