Intelligent Logistics Scheduling Algorithm in Dynamic Traffic Environment

Authors

  • Xiaoqing Wu College of business administration, Chongqing Vocational and Technical University of Mechatronics, Bishan402760, Chongqing, China
  • Xiaoye Du Shandong Transport Vocational College, Weifang 261206, Shandong, China
  • Yanan Liu Shandong Transport Vocational College, Weifang 261206, Shandong, China

Abstract

With the increasingly heavy traffic conditions, the dynamic traffic environment has gradually become a serious problem for the scheduling of logistics vehicle transportation. Traditional heuristic scheduling algorithms are mostly based on static scheduling, lacking adaptability to dynamic environments that involve, for instance, traffic accidents and natural disasters, and failing to fully consider multiple issues such as cost and time for optimization, resulting in low scheduling efficiency. This study used the multi-objective optimization of MAPPO (Multi-Agent Proximal Policy Optimization) and the dynamic information extraction advantages of LSTM (Long Short-term Memory) to study intelligent logistics scheduling under dynamic traffic environments. First, the study matched the traffic monitoring data and Google maps API (Application Programming Interface) data of similar time with the logistics distribution data according to the timestamp, and combined the geocoding of the Geopy library to match the geographic location and records according to the nearest matching method. Subsequently, a mapping relationship between traffic section ID and delivery route section was established, and urban traffic system data and real-time traffic data were linked to each delivery record. Then, the LSTM model was used to capture the dynamic information of the traffic environment, generate predicted traffic flow and congestion conditions, and finally input the traffic flow, speed and other states predicted by LSTM into the MAPPO algorithm model to assist the logistics vehicle intelligent body to dynamically adjust route selection and other scheduling according to traffic conditions. The experiment was based on data from the urban traffic system of the Shenzhen Traffic
Management Center and a logistics center from June to December 2023, and intelligently dispatched logistics vehicles in a dynamic environment. The results showed that in peak traffic flow, the dispatch efficiency of MAPPO-LSTM reached 87.5%, an increase of 3.5% compared to the MAPPO algorithm. The overall satisfaction score reached a high 14 points. Experiments show that the MAPPO-LSTM algorithm has good adaptability to dynamic traffic environments, greatly improves scheduling efficiency, and provides efficient guarantees for intelligent logistics transportation.


Keywords: dynamic traffic environment, intelligent logistics scheduling, MAPPO algorithm, LSTM model, scheduling efficiency

Cite As

X. Wu, X. Du, Y. Liu, "Intelligent Logistics Scheduling Algorithm in Dynamic Traffic Environment", Engineering Intelligent
Systems, vol. 33 no. 6, pp. 631-644, 2025.

 

Downloads

Published

2025-11-01

Issue

Vol. 33 No. 6 (2025): International Journal of Engineering Intelligent Systems

Section

Article

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.