Abstract
Since the report of the 19th National Congress of the Communist Party of China proposed the implementation of the rural revitalization strategy, the rural economy has developed rapidly, the effect of industrial agglomeration has been further deepened, user consumption levels and procurement needs have continued to increase, and logistics activities have increased sharply. The logistics distribution is a key link in the logistics service supply chain, and the study of logistics distribution path planning is of great significance to the development of rural revitalization. This paper aims to study the application of swarm intelligence algorithm in logistics distribution route planning under the background of rural revitalization. This article first expounds the problems and reasons that hinder rural logistics distribution, puts forward the form and optimization goal of logistics distribution path planning, and explains its definition, classification and solution methods respectively. This paper proposes the Intelligent Ant Colony Algorithm (IACA) to solve the logistics distribution path planning and solve the problem of multiple distribution centers. Finally, the algorithm is compared with the Cat Group Algorithm (CGA) for simulation experiments to verify the effectiveness of the algorithm. The experimental results show that when the number of distribution centers is 14, the optimal number of iterations for CGA and IACA is 41 and 30, respectively. When the number of distribution centers is 48, the optimal number of iterations for CGA and IACA is 202, respectively. 100 times, it shows that the algorithm proposed in this paper can be well used in the path planning of logistics distribution, improve the efficiency of distribution, and reduce the cost of logistics distribution.
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Xiao, Y. (2022). Application of Intelligent Ant Colony Algorithm in Rural Logistics Intelligent Distribution Route Planning. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 102. Springer, Singapore. https://doi.org/10.1007/978-981-16-7466-2_1
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DOI: https://doi.org/10.1007/978-981-16-7466-2_1
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