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Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III

The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing desi...

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Main Authors: Liu, Jiajia, Zainudin, Edi Syams, As'arry, Azizan, Ismail, Mohd Idris Shah
Format: Article
Language:English
Published: Universiti Malaysia Pahang Al-Sultan Abdullah Publishing 2024
Online Access:http://psasir.upm.edu.my/id/eprint/114691/1/114691.pdf
http://psasir.upm.edu.my/id/eprint/114691/
https://journal.ump.edu.my/ijame/article/view/10582
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spelling my.upm.eprints.1146912025-01-23T07:22:01Z http://psasir.upm.edu.my/id/eprint/114691/ Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III Liu, Jiajia Zainudin, Edi Syams As'arry, Azizan Ismail, Mohd Idris Shah The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing design and functionality, necessitating the effective management of these emergent 4D printing services. This study aims to bridge the gap between the static nature of existing cloud manufacturing services and the dynamic requirements imposed by 4D printing technology. We propose a comprehensive multiobjective optimization model for cloud-based 4D printing service portfolios, incorporating the intricate complexities of 4D printing services and assessing the efficacy of the Non-Dominated Sorting Genetic Algorithm III (NSGA III) in optimizing these service portfolios to meet dynamic demands. In this research, the NSGA III algorithm is employed to develop a multiobjective optimization framework for 4D printing service portfolios, addressing critical issues such as service cost, time, quality, adaptability, and overall service optimization amidst fluctuating demand and service availability. The findings indicate that the NSGA III algorithm demonstrates superior performance in terms of generational distance and inverted generational distance, particularly excelling in convergence and diversity for high-dimensional optimization problems when compared to the comparison algorithms. The study concludes that the NSGA III algorithm exhibits significant potential in optimizing the orchestration of cloud-based 4D printing service portfolios, underscoring its effectiveness in managing the complexities associated with these services. This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field. Universiti Malaysia Pahang Al-Sultan Abdullah Publishing 2024-09-20 Article PeerReviewed text en cc_by_nc_4 http://psasir.upm.edu.my/id/eprint/114691/1/114691.pdf Liu, Jiajia and Zainudin, Edi Syams and As'arry, Azizan and Ismail, Mohd Idris Shah (2024) Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III. International Journal of Automotive and Mechanical Engineering, 21 (3). pp. 11453-11468. ISSN 2229-8649; eISSN: 2180-1606 https://journal.ump.edu.my/ijame/article/view/10582 10.15282/ijame.21.3.2024.1.0884
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing design and functionality, necessitating the effective management of these emergent 4D printing services. This study aims to bridge the gap between the static nature of existing cloud manufacturing services and the dynamic requirements imposed by 4D printing technology. We propose a comprehensive multiobjective optimization model for cloud-based 4D printing service portfolios, incorporating the intricate complexities of 4D printing services and assessing the efficacy of the Non-Dominated Sorting Genetic Algorithm III (NSGA III) in optimizing these service portfolios to meet dynamic demands. In this research, the NSGA III algorithm is employed to develop a multiobjective optimization framework for 4D printing service portfolios, addressing critical issues such as service cost, time, quality, adaptability, and overall service optimization amidst fluctuating demand and service availability. The findings indicate that the NSGA III algorithm demonstrates superior performance in terms of generational distance and inverted generational distance, particularly excelling in convergence and diversity for high-dimensional optimization problems when compared to the comparison algorithms. The study concludes that the NSGA III algorithm exhibits significant potential in optimizing the orchestration of cloud-based 4D printing service portfolios, underscoring its effectiveness in managing the complexities associated with these services. This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field.
format Article
author Liu, Jiajia
Zainudin, Edi Syams
As'arry, Azizan
Ismail, Mohd Idris Shah
spellingShingle Liu, Jiajia
Zainudin, Edi Syams
As'arry, Azizan
Ismail, Mohd Idris Shah
Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III
author_facet Liu, Jiajia
Zainudin, Edi Syams
As'arry, Azizan
Ismail, Mohd Idris Shah
author_sort Liu, Jiajia
title Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III
title_short Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III
title_full Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III
title_fullStr Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III
title_full_unstemmed Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III
title_sort modeling approach of cloud 4d printing service composition optimization based on non-dominated sorting genetic algorithm iii
publisher Universiti Malaysia Pahang Al-Sultan Abdullah Publishing
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/114691/1/114691.pdf
http://psasir.upm.edu.my/id/eprint/114691/
https://journal.ump.edu.my/ijame/article/view/10582
_version_ 1823093240649220096
score 13.235362

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