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Research on Vehicle and Cargo Loading Mode Based on Improved Ant Colony Algorithm

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Advanced Data Mining and Applications (ADMA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15387))

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Abstract

In this paper, we address the low matching efficiency and information asymmetry in vehicle and cargo matching by constructing a model based on actual complex business scenarios. This model accurately matches vehicle and cargo characteristics, improves overall matching efficiency, reduces idling rates, and optimizes matching revenue and transportation costs for drivers. An improved ant colony algorithm is proposed, incorporating a dynamic pheromone updating strategy, adaptive selection probability adjustment, and path diversity evaluation to enhance global search efficiency. Applied to real data from the Full Truck Alliance platform, the experimental results show the improved algorithm outperforms traditional and other intelligent optimization algorithms in efficiency and stability. This work offers a new solution for vehicle and cargo allocation and a valuable reference for applying intelligent optimization algorithms in logistics.

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Author information

Authors and Affiliations

  1. Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing, 210023, People’s Republic of China

    Zewei Zhao, Zhixin Sun & Zhe Sun

  2. Post Big Data Technology and Application Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Zewei Zhao, Zhixin Sun & Zhe Sun

  3. Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, 210023, People’s Republic of China

    Zewei Zhao, Zhixin Sun & Zhe Sun

Authors
  1. Zewei Zhao
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  2. Zhixin Sun
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  3. Zhe Sun
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Corresponding author

Correspondence to Zewei Zhao .

Editor information

Editors and Affiliations

  1. Macquarie University, Sydney, NSW, Australia

    Quan Z. Sheng

  2. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  3. Australian National University, Canberra, ACT, Australia

    Jing Jiang

  4. Macquarie University, Sydney, NSW, Australia

    Xuyun Zhang

  5. The University of Adelaide, Adelaide, SA, Australia

    Wei Emma Zhang

  6. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  7. Macquarie University, Sydney, NSW, Australia

    Jia Wu

  8. University of Dubai, Dubai, United Arab Emirates

    Wathiq Mansoor

  9. Macquarie University, Sydney, NSW, Australia

    Congbo Ma

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© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhao, Z., Sun, Z., Sun, Z. (2025). Research on Vehicle and Cargo Loading Mode Based on Improved Ant Colony Algorithm. In: Sheng, Q.Z., et al. Advanced Data Mining and Applications. ADMA 2024. Lecture Notes in Computer Science(), vol 15387. Springer, Singapore. https://doi.org/10.1007/978-981-96-0811-9_19

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  • DOI: https://doi.org/10.1007/978-981-96-0811-9_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-0810-2

  • Online ISBN: 978-981-96-0811-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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