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<p>Solving logistics and distribution problems through intelligent algorithms can strongly improve the market core competitiveness of e-commerce logistics enterprises. This paper maps the distribution station location in e-commerce logistics to graph theory nodes and establishes the logistics path planning model. Then use the binary group to improve the Dicoscher algorithm to solve the planning of the e-commerce logistics path, and through the genetic algorithm to solve the objective function of the cost and other factors in the logistics scheduling to achieve the logistics cost concessions, and ultimately build the e-commerce logistics planning and scheduling optimization strategy. The analysis of the application effect of constructing the path planning and optimization strategy finds that the distribution distance of the logistics path solved by this paper’s algorithm under the condition of 20 distribution points is 80.47km shorter than that of the ant colony algorithm. The total cost spent on the path is 799.75 yuan on average, and the time consumed is only 62.14s. Meanwhile, it has been found that after implementing the path planning strategy, the total working time of the dispatcher decreases by 28.6 hours, and the pressure of the job significantly decreases. The e-commerce logistics path planning and scheduling optimization method designed in this paper provides an effective solution for cost-saving in logistics enterprises.</p>
</abstract>

Solving logistics and distribution problems through intelligent algorithms can strongly improve the market core competitiveness of e-commerce logistics enterprises. This paper maps the distribution station location in e-commerce logistics to graph theory nodes and establishes the logistics path planning model. Then use the binary group to improve the Dicoscher algorithm to solve the planning of the e-commerce logistics path, and through the genetic algorithm to solve the objective function of the cost and other factors in the logistics scheduling to achieve the logistics cost concessions, and ultimately build the e-commerce logistics planning and scheduling optimization strategy. The analysis of the application effect of constructing the path planning and optimization strategy finds that the distribution distance of the logistics path solved by this paper’s algorithm under the condition of 20 distribution points is 80.47km shorter than that of the ant colony algorithm. The total cost spent on the path is 799.75 yuan on average, and the time consumed is only 62.14s. Meanwhile, it has been found that after implementing the path planning strategy, the total working time of the dispatcher decreases by 28.6 hours, and the pressure of the job significantly decreases. The e-commerce logistics path planning and scheduling optimization method designed in this paper provides an effective solution for cost-saving in logistics enterprises.

Intelligent e-commerce logistics path planning and scheduling optimization combined with graph theory

Applied Mathematics and Nonlinear Sciences

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

Solving logistics and distribution problems through intelligent algorithms can strongly improve the market core competitiveness of e-commerce logistics...

Intelligent e-commerce logistics path planning and scheduling optimization combined with graph theory

Published Online: May 30, 2024

Page range: -

Received: Feb 03, 2024

Accepted: Apr 14, 2024

DOI: https://doi.org/10.2478/amns-2024-1262

Keywords
Graph theory, Binary group, Genetic algorithm, Kosher algorithm, Path planning

© 2024 Rui Li, et al., published by Sciendo

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

Intelligent e-commerce logistics path planning and scheduling optimization combined with graph theory

Published Online: May 30, 2024

Page range: -

Received: Feb 03, 2024

Accepted: Apr 14, 2024

DOI: https://doi.org/10.2478/amns-2024-1262

KeywordsGraph theory, Binary group, Genetic algorithm, Kosher algorithm, Path planning

© 2024 Rui Li, et al., published by Sciendo

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

Keywords
Graph theory, Binary group, Genetic algorithm, Kosher algorithm, Path planning