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The optimization of automated goods dynamic allocation and warehousing model
Z.K. Hou 1

1 Sichuan College of Architectural Technology, Deyang, Sichuan 618000, China

 PDF, 1393 kB

DOI: 10.18287/2412-6179-CO-682

Pages: 843-847.

Abstract:
In the development of modern logistics, the role of automated cargo warehousing is gradually reflected, which is essential for the automatic distribution of goods. This paper briefly introduced the automatic location allocation model and the particle swarm optimization (PSO) algorithm used to optimize the model. At the same time, it introduced the concept of genetic operator and multi-group co-evolution to improve the algorithm, and then the simulation analysis of standard PSO and improved PSO was performed on MATLAB software. The results showed that the improved PSO iterated fewer times and get better solution sets; compared with the manual allocation scheme, the improved PSO calculation reduced more warehousing time, lowered more center of gravity height, and improved shelf stability. In summary, the improved PSO algorithm can effectively optimize the automated goods dynamic allocation and warehousing model.

Keywords:
location allocation, particle swarm optimization, genetic operator, multi-group co-evolution.

Citation:
Hou ZK. The optimization of automated goods dynamic allocation and warehousing model. Computer Optics 2020; 44(5): 843-847. DOI: 10.18287/2412-6179-CO-682.

Acknowledgements:
This study was supported by 2016 Special Task of Scientific and Technological Research in Sichuan College of Architectural Technology: Research and Design on Small Automatic Sorting and Accessing Stereo Warehouse in University Jingdong Delivery Based on Jingdong Small Parcel Logistics Data (2016KJ36).

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