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

<p>The evolution of the logistics and distribution industry, notably the express delivery sector, has significantly increased its prevalence in everyday life. This escalation necessitates an ongoing innovation in logistics strategies and enhancements in service quality, positioning these elements at the heart of industry focus. This study initially addresses the vehicular route planning issue within logistics distribution, selecting Company A as a case study to examine inherent logistical challenges. Subsequently, it develops an optimal route planning model under time constraints. It is converted into a mixed-integer linear programming model through techniques such as variable substitution and segmented linear approximation. This conversion facilitates rapid solutions by mathematical planning solvers. The research contrasts the logistics and distribution performance before and after optimization to assess the efficacy of the proposed strategies. Findings indicate that optimized vehicular route planning achieves a more equitable distribution of delivery tasks, along with substantial improvements in the complexity of transportation routes and the reduction of travel distances. Specifically, the optimization results in a significant decrease in the penalty costs associated with the vehicles, with the penalty cost for the third vehicle involved in the study reduced by 66.40%. This evidence supports the implementation of scientific vehicle scheduling strategies by logistics firms, underscoring the model’s practical implications.</p>
</abstract>

The evolution of the logistics and distribution industry, notably the express delivery sector, has significantly increased its prevalence in everyday life. This escalation necessitates an ongoing innovation in logistics strategies and enhancements in service quality, positioning these elements at the heart of industry focus. This study initially addresses the vehicular route planning issue within logistics distribution, selecting Company A as a case study to examine inherent logistical challenges. Subsequently, it develops an optimal route planning model under time constraints. It is converted into a mixed-integer linear programming model through techniques such as variable substitution and segmented linear approximation. This conversion facilitates rapid solutions by mathematical planning solvers. The research contrasts the logistics and distribution performance before and after optimization to assess the efficacy of the proposed strategies. Findings indicate that optimized vehicular route planning achieves a more equitable distribution of delivery tasks, along with substantial improvements in the complexity of transportation routes and the reduction of travel distances. Specifically, the optimization results in a significant decrease in the penalty costs associated with the vehicles, with the penalty cost for the third vehicle involved in the study reduced by 66.40%. This evidence supports the implementation of scientific vehicle scheduling strategies by logistics firms, underscoring the model’s practical implications.

Optimization strategy of mixed-integer linear planning in logistics distribution

University of Shanghai for Science and Technology

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Optimization strategy of mixed-integer linear planning in logistics distribution"}

The evolution of the logistics and distribution industry, notably the express delivery sector, has significantly increased its prevalence in everyday life....

Optimization strategy of mixed-integer linear planning in logistics distribution

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 04. Apr. 2024

Akzeptiert: 28. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1904

Schlüsselwörter
Mixed integer linear programming, Path planning, Time constraint, Logistics distribution

© 2024 Yuhua Li, published by Sciendo

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

Optimization strategy of mixed-integer linear planning in logistics distribution

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 04. Apr. 2024

Akzeptiert: 28. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1904

SchlüsselwörterMixed integer linear programming, Path planning, Time constraint, Logistics distribution

© 2024 Yuhua Li, published by Sciendo

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

Schlüsselwörter
Mixed integer linear programming, Path planning, Time constraint, Logistics distribution