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This article is cited in 2 scientific papers (total in 2 papers)
NUMERICAL METHODS AND DATA ANALYSIS
The optimization of automated goods dynamic allocation and warehousing model
Zh. Hou Sichuan College of Architectural Technology, Deyang, Sichuan 618000, China
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.
Received: 23.12.2019 Accepted: 15.02.2020
Citation:
Zh. Hou, “The optimization of automated goods dynamic allocation and warehousing model”, Computer Optics, 44:5 (2020), 843–847
Linking options:
https://www.mathnet.ru/eng/co854 https://www.mathnet.ru/eng/co/v44/i5/p843
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Abstract page: | 66 | Full-text PDF : | 56 | References: | 12 |
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