Research on vehicle-cargo matching problem based on deep Q-network

Wenju Zhang

doi:10.1117/12.2689800

7 September 2023 Research on vehicle-cargo matching problem based on deep Q-network

Wenju Zhang

Proceedings Volume 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023); 127901R (2023) https://doi.org/10.1117/12.2689800
Event: 8th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 2023, Hangzhou, China

Abstract

The logistics industry is crucial to the global economy due to economic globalization and e-commerce. Matching vehicles and goods is a bottleneck problem in logistics, and improving the accuracy and efficiency of truck-cargo matching is a research hotspot. This study proposes a vehicle-cargo matching method based on deep learning using a deep reinforcement learning algorithm to train an agent. The Q-learning algorithm is used to train the agent, taking vehicle and source data as input, and outputting the matching scheme. The results show that this method has high timeliness, shortening matching time, reducing matching cost, and improving logistics efficiency and service quality.

Citation Download Citation

Wenju Zhang "Research on vehicle-cargo matching problem based on deep Q-network", Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 127901R (7 September 2023); https://doi.org/10.1117/12.2689800

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