Paper
14 February 2024 Analysis model for emergency transportation and structural optimization in logistics
Dan Han, Fan Yang, Zhanjiang Gao
Author Affiliations +
Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 1301826 (2024) https://doi.org/10.1117/12.3024060
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China
Abstract
Due to the impact of holidays and promotions, e-commerce users' order volumes fluctuate significantly. In addition, sudden events such as epidemics and earthquakes may lead to the temporary or permanent closure of logistics facilities, resulting in parcels being urgently diverted to other logistics locations. These factors will affect the number of parcels transported on different routes and the number of parcels handled by different logistics locations. To address these issues, this paper constructs an emergency transportation and structural optimization model for logistics, focusing on the relationship between logistics facilities and transportation routes in e-commerce logistics networks. By applying mathematical models and algorithms, the volume of goods in each logistics facility and route is predicted to propose an optimised plan for transportation and sorting. The proposed model is validated and implemented using real e-commerce logistics data and existing statistical data. The results provide important theoretical guidance and decision support for ecommerce logistics networks, helping to reduce operational risks and improve operational efficiency.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Dan Han, Fan Yang, and Zhanjiang Gao "Analysis model for emergency transportation and structural optimization in logistics", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 1301826 (14 February 2024); https://doi.org/10.1117/12.3024060
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KEYWORDS
Mathematical optimization

Transportation

Data modeling

Neural networks

Industry

Sustainability

Analytical research

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