Research on intelligent logistics demand path analysis based on VAR model based on improved ant colony algorithm

Yong Zhang

doi:10.1117/12.2653159

20 October 2022 Research on intelligent logistics demand path analysis based on VAR model based on improved ant colony algorithm

Yong Zhang

Author Affiliations +

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123502B (2022) https://doi.org/10.1117/12.2653159
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

In order to quickly solve the optimal path of logistics transportation in complex traffic environment, a logistics transportation path optimization model based on improved ant colony algorithm is proposed on the basis of traditional ant colony algorithm. First, by adding constraints based on transportation time, cost, and average road smoothness factor in the traditional ant colony algorithm, and at the same time improving the traditional pheromone update method. The pheromone concentration on the road is subject to a minimum and maximum limit, thereby changing the routing transition probability. Finally, the experimental data using the improved ant colony algorithm, the CSAACO algorithm and the ACO algorithm show that the improved ant colony algorithm is significantly lower than the CSAACO algorithm and the ACO algorithm in terms of transportation distance and transportation time. The improved ant colony algorithm has stronger global optimization ability, converges faster, takes less time and obtains a shorter optimal path, which improves the transportation efficiency of the entire logistics industry.

Citation Download Citation

Yong Zhang "Research on intelligent logistics demand path analysis based on VAR model based on improved ant colony algorithm", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123502B (20 October 2022); https://doi.org/10.1117/12.2653159

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