Research on smart logistics transportation capacity planning based on big data-taking enterprise A as an example

Zonglei Hu; Xinghong Li; Huaqiong Liu

doi:10.1117/12.2652354

21 November 2022 Research on smart logistics transportation capacity planning based on big data-taking enterprise A as an example

Zonglei Hu, Xinghong Li, Huaqiong Liu

Author Affiliations +

Proceedings Volume 12340, International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022); 1234016 (2022) https://doi.org/10.1117/12.2652354
Event: International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022), 2022, Lanzhou, China

Abstract

Based on big data computing, this paper deeply studies the work of the transportation capacity planning part of the smart logistics platform. As the core management object of the intelligent logistics platform, transportation capacity needs to be predicted, monitored and optimized from multiple dimensions such as waybill release, real-time vehicle supervision, and vehicle and cargo matching. Through the analysis of relevant needs and practical difficulties in the previous investigation and investigation of Nanchang regional logistics enterprise A, and combined with the development trend of new technologies, this paper proposes a solution of " transportation capacity collaborative intelligent logistics platform" based on big data. The construction of the platform depends to a certain extent on the final data format and usage scenarios. Therefore, this paper focuses on the establishment of relevant statistical data and portrait models for the data entities often used in the logistics process to provide support for the construction of the smart logistics platform.

Citation Download Citation

Zonglei Hu, Xinghong Li, and Huaqiong Liu "Research on smart logistics transportation capacity planning based on big data-taking enterprise A as an example", Proc. SPIE 12340, International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022), 1234016 (21 November 2022); https://doi.org/10.1117/12.2652354

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