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Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data

In order to improve the efficiency of cloth laying and cutting integrated production process, this article proposes a method of optimal scheduling of cloth laying and cutting garment production system process based on big data and genetic algorithm. The chromosomes in the algorithm are expressed by...

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Detalles Bibliográficos
Autores principales: Sun, Ning, Cao, Botao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420574/
https://www.ncbi.nlm.nih.gov/pubmed/36045981
http://dx.doi.org/10.1155/2022/2293473
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author Sun, Ning
Cao, Botao
author_facet Sun, Ning
Cao, Botao
author_sort Sun, Ning
collection PubMed
description In order to improve the efficiency of cloth laying and cutting integrated production process, this article proposes a method of optimal scheduling of cloth laying and cutting garment production system process based on big data and genetic algorithm. The chromosomes in the algorithm are expressed by real strings. The method of bit string crossover and mutation is used to solve the premature problem of the algorithm. The experimental results show that the actual cutting time of the plan is 736 min, and the total idle time is 113 min. The idle time occurs in processes 25, 28, 34, 35, and 31, respectively. The cutting time of the plan arranged by the genetic algorithm is 627 min, and there is no idle time. Conclusion. This method can effectively solve the optimal scheduling problem of the cloth laying and cutting production process.
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spelling pubmed-94205742022-08-30 Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data Sun, Ning Cao, Botao Comput Intell Neurosci Research Article In order to improve the efficiency of cloth laying and cutting integrated production process, this article proposes a method of optimal scheduling of cloth laying and cutting garment production system process based on big data and genetic algorithm. The chromosomes in the algorithm are expressed by real strings. The method of bit string crossover and mutation is used to solve the premature problem of the algorithm. The experimental results show that the actual cutting time of the plan is 736 min, and the total idle time is 113 min. The idle time occurs in processes 25, 28, 34, 35, and 31, respectively. The cutting time of the plan arranged by the genetic algorithm is 627 min, and there is no idle time. Conclusion. This method can effectively solve the optimal scheduling problem of the cloth laying and cutting production process. Hindawi 2022-08-21 /pmc/articles/PMC9420574/ /pubmed/36045981 http://dx.doi.org/10.1155/2022/2293473 Text en Copyright © 2022 Ning Sun and Botao Cao. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Sun, Ning
Cao, Botao
Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data
title Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data
title_full Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data
title_fullStr Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data
title_full_unstemmed Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data
title_short Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data
title_sort production scheduling optimization of garment intelligent manufacturing system based on big data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420574/
https://www.ncbi.nlm.nih.gov/pubmed/36045981
http://dx.doi.org/10.1155/2022/2293473
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