<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The limited nature of logistics and distribution vehicles and the variability of customer acceptance service time limit the service efficiency and quality of logistics and distribution. To optimize a logistics distribution path under capacity and time window constraints, a mathematical model of the problem is first developed in this study, with the lowest cost as the model’s objective function. The logistics distribution issue with soft time windows is then addressed using an ant colony algorithm, and a logistics path optimization strategy based on the maximum minimal ant colony system is suggested. Then, the heuristic function is rebuilt to improve the ant colony algorithm’s solution speed, and the pheromone update approach is included. Finally, experimental approaches are used to test the model’s and optimization algorithm’s efficacy for customer sizes of 30, 50, and 100. The experimental results show that the optimized ant colony algorithm has the best value of 2 for α and 3 for β, which can converge earlier. The improved ant colony algorithm also finds the best solution faster than the conventional ant colony method in just 23 rounds. In the mathematical model of the logistics distribution path optimization issue, this study suggests that the optimized ant colony method has the optimization algorithm’s rationality, efficacy, and stability.</p>
</abstract>

The limited nature of logistics and distribution vehicles and the variability of customer acceptance service time limit the service efficiency and quality of logistics and distribution. To optimize a logistics distribution path under capacity and time window constraints, a mathematical model of the problem is first developed in this study, with the lowest cost as the model’s objective function. The logistics distribution issue with soft time windows is then addressed using an ant colony algorithm, and a logistics path optimization strategy based on the maximum minimal ant colony system is suggested. Then, the heuristic function is rebuilt to improve the ant colony algorithm’s solution speed, and the pheromone update approach is included. Finally, experimental approaches are used to test the model’s and optimization algorithm’s efficacy for customer sizes of 30, 50, and 100. The experimental results show that the optimized ant colony algorithm has the best value of 2 for α and 3 for β, which can converge earlier. The improved ant colony algorithm also finds the best solution faster than the conventional ant colony method in just 23 rounds. In the mathematical model of the logistics distribution path optimization issue, this study suggests that the optimized ant colony method has the optimization algorithm’s rationality, efficacy, and stability.

Dynamic Route Optimization for Chinese E-Commerce Logistics Based on Ant Colony Algorithm

Applied Mathematics and Nonlinear Sciences

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

The limited nature of logistics and distribution vehicles and the variability of customer acceptance service time limit the service efficiency and quality of...

Dynamic Route Optimization for Chinese E-Commerce Logistics Based on Ant Colony Algorithm

Online veröffentlicht: 09. Sept. 2023

Seitenbereich: -

Eingereicht: 11. Nov. 2022

Akzeptiert: 20. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00325

Schlüsselwörter
Logistics and distribution, Time window constraint, Ant colony algorithm, Logistics path optimization, Heuristic function, Pheromone update

© 2023 Ximin Zhou, published by Sciendo

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

Dynamic Route Optimization for Chinese E-Commerce Logistics Based on Ant Colony Algorithm

Online veröffentlicht: 09. Sept. 2023

Seitenbereich: -

Eingereicht: 11. Nov. 2022

Akzeptiert: 20. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00325

SchlüsselwörterLogistics and distribution, Time window constraint, Ant colony algorithm, Logistics path optimization, Heuristic function, Pheromone update

© 2023 Ximin Zhou, published by Sciendo

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

Schlüsselwörter
Logistics and distribution, Time window constraint, Ant colony algorithm, Logistics path optimization, Heuristic function, Pheromone update