Abstract
As cities expand and the pace of life accelerates, modern logistics services need to explore new air routes. While air logistics is fast and convenient, it also faces many problems, such as time-sensitive dynamic order requirements, the limited battery power of unmanned aerial vehicle (UAV) and so on. Besides, UAVs need to cooperate and make decisions in real time to meet city-wide logistics needs. However, limited by the computing power, it is difficult to process massive logistics demands in real time. In this paper, we propose an Air Logistics Service-Oriented Digital Twin Network based on collaborative decision model, called AlsoDTN. Firstly, in order allocation task, we establish an information fusion mechanism based on Transformer architecture to obtain the optimal order. Secondly, to adapt to the long-term route planning task, we use multi-agent deep reinforcement learning technology to make UAVs cooperate with each other. Experimental results show that our algorithm has an improvement of 18% relative to baseline algorithm.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grants (62071067, 62171057, 62101064, 62001054), in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center.
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Fu, Q., Chen, D., Sun, H., Qi, Q., Wang, J., Liao, J. (2023). AlsoDTN: An Air Logistics Service-Oriented Digital Twin Network Based on Collaborative Decision Model. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_40
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DOI: https://doi.org/10.1007/978-3-031-26507-5_40
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