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<p>Under the advancement of reform and opening-up policy, the development direction of e-commerce enterprises has gradually shifted from the city to the rural areas with lower levels of economic development. This paper builds a rural e-commerce intelligent logistics system based on the Internet of Things (IoT) technology and proposes an improved ant colony algorithm to plan the optimal path of rural e-commerce logistics and distribution by mining the transfer probability of each distribution point in logistics and distribution as well as pheromone updating mechanism, so as to improve the logistics efficiency and customer satisfaction. Experiments show that the improved ACO algorithm in this paper performs significantly better than the genetic algorithm and particle swarm algorithm in logistics and distribution path planning and can control the transportation cost and penalty cost at a lower level. When considering the cargo loss of fresh products in logistics distribution, the satisfaction rate of traditional logistics distribution is only 65.2%, while the satisfaction rate of logistics distribution based on intelligent logistics technology is 87.9% on average. The distribution path planned by the ant colony algorithm has less mileage, lower cost, and higher logistics efficiency. This paper provides a feasible path for further improvement of rural e-commerce logistics efficiency, which can promote the development of the logistics industry in the direction of more intelligent, personalized, and green.</p>
</abstract>

Under the advancement of reform and opening-up policy, the development direction of e-commerce enterprises has gradually shifted from the city to the rural areas with lower levels of economic development. This paper builds a rural e-commerce intelligent logistics system based on the Internet of Things (IoT) technology and proposes an improved ant colony algorithm to plan the optimal path of rural e-commerce logistics and distribution by mining the transfer probability of each distribution point in logistics and distribution as well as pheromone updating mechanism, so as to improve the logistics efficiency and customer satisfaction. Experiments show that the improved ACO algorithm in this paper performs significantly better than the genetic algorithm and particle swarm algorithm in logistics and distribution path planning and can control the transportation cost and penalty cost at a lower level. When considering the cargo loss of fresh products in logistics distribution, the satisfaction rate of traditional logistics distribution is only 65.2%, while the satisfaction rate of logistics distribution based on intelligent logistics technology is 87.9% on average. The distribution path planned by the ant colony algorithm has less mileage, lower cost, and higher logistics efficiency. This paper provides a feasible path for further improvement of rural e-commerce logistics efficiency, which can promote the development of the logistics industry in the direction of more intelligent, personalized, and green.

Mechanism Analysis of Smart Logistics Technology to Enhance the Logistics Efficiency of Rural E-Commerce

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under the advancement of reform and opening-up policy, the development direction of e-commerce enterprises has gradually shifted from the city to the rural...

Mechanism Analysis of Smart Logistics Technology to Enhance the Logistics Efficiency of Rural E-Commerce

Publié en ligne: 05 août 2024

Pages: -

Reçu: 13 avr. 2024

Accepté: 29 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2070

Mots clés
Internet of things, Ant colony algorithm, Transfer probability, Pheromone, Rural e-commerce logistics

© 2024 Linna Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mechanism Analysis of Smart Logistics Technology to Enhance the Logistics Efficiency of Rural E-Commerce

Publié en ligne: 05 août 2024

Pages: -

Reçu: 13 avr. 2024

Accepté: 29 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2070

Mots clésInternet of things, Ant colony algorithm, Transfer probability, Pheromone, Rural e-commerce logistics

© 2024 Linna Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Internet of things, Ant colony algorithm, Transfer probability, Pheromone, Rural e-commerce logistics