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Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing

In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the...

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Detalles Bibliográficos
Autores principales: James, C. D., Mondal, Sandeep
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785933/
https://www.ncbi.nlm.nih.gov/pubmed/33424134
http://dx.doi.org/10.1007/s00521-020-05657-1
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author James, C. D.
Mondal, Sandeep
author_facet James, C. D.
Mondal, Sandeep
author_sort James, C. D.
collection PubMed
description In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00521-020-05657-1.
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spelling pubmed-77859332021-01-06 Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing James, C. D. Mondal, Sandeep Neural Comput Appl Original Article In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00521-020-05657-1. Springer London 2021-01-06 2021 /pmc/articles/PMC7785933/ /pubmed/33424134 http://dx.doi.org/10.1007/s00521-020-05657-1 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
James, C. D.
Mondal, Sandeep
Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
title Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
title_full Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
title_fullStr Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
title_full_unstemmed Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
title_short Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
title_sort optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785933/
https://www.ncbi.nlm.nih.gov/pubmed/33424134
http://dx.doi.org/10.1007/s00521-020-05657-1
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