Scheduling by NSGA-II: Review and Bibliometric Analysis

Rahimi, I; Gandomi, AH; Deb, K; Chen, F; Nikoo, MR

Scheduling by NSGA-II: Review and Bibliometric Analysis

Rahimi, I Gandomi, AH Deb, K Chen, F

Nikoo, MR

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Processes, 2022, 10, (1)
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Rahimi, I
dc.contributor.author	Gandomi, AH
dc.contributor.author	Deb, K
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Nikoo, MR
dc.date.accessioned	2023-03-27T04:15:12Z
dc.date.available	2023-03-27T04:15:12Z
dc.date.issued	2022-01-01
dc.identifier.citation	Processes, 2022, 10, (1)
dc.identifier.issn	2227-9717
dc.identifier.issn	2227-9717
dc.identifier.uri	http://hdl.handle.net/10453/168579
dc.description.abstract	NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Processes
dc.relation.isbasedon	10.3390/pr10010098
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0904 Chemical Engineering
dc.title	Scheduling by NSGA-II: Review and Bibliometric Analysis
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0904 Chemical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-27T04:15:10Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	1

Abstract:

NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.

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http://hdl.handle.net/10453/168579