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

<p>Warehousing space layout is crucial for e-commerce logistics. A refined and scientific approach to warehouse design enhances space utilization and operational efficiency. This minimizes unused space and idle time, lowers inventory costs, and strengthens the competitive edge of e-commerce logistics. In this study, we conceptualize the logistics space layout issue as a crate problem, harnessing the rapid optimization capabilities of genetic algorithms (GA) and simulated annealing (SA). We propose a hybrid algorithm where SA forms the core, using GA to generate initial solutions and new iterations. Design parameters for the combined operation process are systematically developed. This algorithm is evaluated using both datasets and real arithmetic cases, demonstrating superior performance in large-scale combinatorial optimization problems. It achieves a search accuracy of 0.5% to 3% higher than GA alone, converging more reliably to the global optimum, thus reducing search time and operational scope. This research offers vital theoretical insights for optimizing space layouts in e-commerce logistics management.</p>
</abstract>

Warehousing space layout is crucial for e-commerce logistics. A refined and scientific approach to warehouse design enhances space utilization and operational efficiency. This minimizes unused space and idle time, lowers inventory costs, and strengthens the competitive edge of e-commerce logistics. In this study, we conceptualize the logistics space layout issue as a crate problem, harnessing the rapid optimization capabilities of genetic algorithms (GA) and simulated annealing (SA). We propose a hybrid algorithm where SA forms the core, using GA to generate initial solutions and new iterations. Design parameters for the combined operation process are systematically developed. This algorithm is evaluated using both datasets and real arithmetic cases, demonstrating superior performance in large-scale combinatorial optimization problems. It achieves a search accuracy of 0.5% to 3% higher than GA alone, converging more reliably to the global optimum, thus reducing search time and operational scope. This research offers vital theoretical insights for optimizing space layouts in e-commerce logistics management.

Spatial layout and optimization of e-commerce logistics management based on combinatorial optimization algorithm

Applied Mathematics and Nonlinear Sciences

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

Warehousing space layout is crucial for e-commerce logistics. A refined and scientific approach to warehouse design enhances space utilization and operational...

Spatial layout and optimization of e-commerce logistics management based on combinatorial optimization algorithm

Pubblicato online: 22 mag 2024

Pagine: -

Ricevuto: 07 feb 2024

Accettato: 08 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1175

Parole chiave
Combinatorial optimization, Simulated annealing, Genetic algorithm, Creating problem, Logistics management

© 2024 Hao Ma, published by Sciendo

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

Spatial layout and optimization of e-commerce logistics management based on combinatorial optimization algorithm

Pubblicato online: 22 mag 2024

Pagine: -

Ricevuto: 07 feb 2024

Accettato: 08 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1175

Parole chiaveCombinatorial optimization, Simulated annealing, Genetic algorithm, Creating problem, Logistics management

© 2024 Hao Ma, published by Sciendo

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

Parole chiave
Combinatorial optimization, Simulated annealing, Genetic algorithm, Creating problem, Logistics management