Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 6th International Conference on Computing and Data Science

Series Vol. 69 , 08 July 2024


Open Access | Article

Optimization of logistics cargo tracking and transportation efficiency based on data science deep learning models

Ang Li * 1 , Shikai Zhuang 2 , Tianyi Yang 3 , Wenran Lu 4 , Jiahao Xu 5
1 Business Analytics, University College Dublin, Dublin, Ireland
2 Electrical Engineering, University of Washington, Seattle, WA, USA
3 Financial Risk Management, University of Connecticut, Stamford CT, USA
4 Electrical Engineering, University of Texas at Austin, Austin, TX, USA
5 Master of Science in Financial Engineering, University of Southern California, CA, USA

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 69, 71-77
Published 08 July 2024. © 08 July 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Ang Li, Shikai Zhuang, Tianyi Yang, Wenran Lu, Jiahao Xu. Optimization of logistics cargo tracking and transportation efficiency based on data science deep learning models. ACE (2024) Vol. 69: 71-77. DOI: 10.54254/2755-2721/69/20241522.

Abstract

With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. These research results provide important support to further promote the intelligent development of the logistics industry.

Keywords

Intelligent logistics algorithms, path optimization, deep reinforcement learning, data mining, transport efficiency optimization

References

1. Oliveira, R. R., Cardoso, I. M., Barbosa, J. L., Da Costa, C. A., & Prado, M. P. (2015). An intelligent model for logistics management based on geofencing algorithms and RFID technology. Expert Systems with Applications, 42(15-16), 6082-6097.

2. Kumar, R. S., Rani, C., & Kumar, P. G. (2018). Design of smart logistics transportation system using MapReduce intelligent water drops algorithm in Hadoop environment. International Journal of Logistics Systems and Management, 31(2), 249-266.

3. Mohamed M. Toward Smart Logistics: Hybridization of Intelligence Techniques of Machine Learning and Multi-Criteria Decision-Making in Logistics 5.0[J]. Multicriteria algorithms with applications, 2023, 1: 42-57.

4. Singh M K, Parhi D R. Path optimization of a mobile robot using an artificial neural network controller[J]. International Journal of Systems Science, 2011, 42(1): 107-120.

5. Dhand, A., Lang, C. E., Luke, D. A., Kim, A., Li, K., McCafferty, L., ... & Lee, J. M. (2019). Social network mapping and functional recovery within 6 months of ischemic stroke. Neurorehabilitation and neural repair, 33(11), 922-932.

6. Sarkis, R. A., Goksen, Y., Mu, Y., Rosner, B., & Lee, J. W. (2018). Cognitive and fatigue side effects of anti-epileptic drugs: an analysis of phase III add-on trials. Journal of Neurology, 265(9), 2137-2142.

7. Pióro M, Szentesi Á, Harmatos J, et al. On open shortest path first related network optimization problems[J]. Performance evaluation, 2002, 48(1-4): 201-223.

8. Arulkumaran K, Deisenroth M P, Brundage M, et al. Deep reinforcement learning: A brief survey[J]. IEEE Signal Processing Magazine, 2017, 34(6): 26-38.

9. Rosner, B., Tamimi, R. M., Kraft, P., Gao, C., Mu, Y., Scott, C., ... & Colditz, G. A. (2021). Simplified breast risk tool integrating questionnaire risk factors, mammographic density, and polygenic risk score: development and validation. Cancer Epidemiology, Biomarkers & Prevention, 30(4), 600-607.

10. Li, Shengbo Eben. "Deep reinforcement learning." Reinforcement learning for sequential decision and optimal control. Singapore: Springer Nature Singapore, 2023. 365-402

11. Allman, R., Mu, Y., Dite, G. S., Spaeth, E., Hopper, J. L., & Rosner, B. A. (2023). Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density, and polygenic risk. Breast Cancer Research and Treatment, 198(2), 335-347.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 6th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-459-0
ISBN (Online)
978-1-83558-460-6
Published Date
08 July 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/69/20241522
Copyright
08 July 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated