Evolutionary Computation in Scheduling: A Scientometric Analysis

Gandomi, AH; Emrouznejad, A; Rahimi, I

Evolutionary Computation in Scheduling: A Scientometric Analysis

Gandomi, AH Emrouznejad, A Rahimi, I

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Publisher:: Wiley
Publication Type:: Chapter
Citation:: Evolutionary computation in scheduling, 2020, pp. 1-10
Issue Date:: 2020-04-17

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Field	Value	Language
dc.contributor.author	Gandomi, AH
dc.contributor.author	Emrouznejad, A
dc.contributor.author	Rahimi, I
dc.date.accessioned	2021-02-07T21:15:16Z
dc.date.available	2021-02-07T21:15:16Z
dc.date.issued	2020-04-17
dc.identifier.citation	Evolutionary computation in scheduling, 2020, pp. 1-10
dc.identifier.isbn	9781119573845
dc.identifier.uri	http://hdl.handle.net/10453/145906
dc.description.abstract	Scheduling and planning problems are generally complex, large‐scale, challenging topics, and involve numerous constraints. To discover a real solution for these problems, most real‐world problems must be formulated as discrete or mixed variable optimization problems. In this study, scientometric analysis is used to analyze scientific literature in this field. Here, scientometric mapping technique illustrates who works on what area, and it is attained by cognitive and density maps and network visualization. The chapter presents those research areas which are most inspired by evolutionary computation (EC) in scheduling. Also, the chapter presents a scientometric mapping analysis to identify who works on what in the field of EC in scheduling. This goal is attained by the visualization of different maps and networks. Finally, summary, conclusions, and future research in the field are presented
dc.format.extent	10
dc.language	en
dc.publisher	Wiley
dc.relation.ispartof	Evolutionary computation in scheduling
dc.relation.isbasedon	10.1002/9781119574293.ch1
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Evolutionary Computation in Scheduling: A Scientometric Analysis
dc.type	Chapter
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/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-02-07T21:14:49Z
pubs.place-of-publication	USA
pubs.publication-status	Published
dc.location	USA

Abstract:

Scheduling and planning problems are generally complex, large‐scale, challenging topics, and involve numerous constraints. To discover a real solution for these problems, most real‐world problems must be formulated as discrete or mixed variable optimization problems. In this study, scientometric analysis is used to analyze scientific literature in this field. Here, scientometric mapping technique illustrates who works on what area, and it is attained by cognitive and density maps and network visualization. The chapter presents those research areas which are most inspired by evolutionary computation (EC) in scheduling. Also, the chapter presents a scientometric mapping analysis to identify who works on what in the field of EC in scheduling. This goal is attained by the visualization of different maps and networks. Finally, summary, conclusions, and future research in the field are presented

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http://hdl.handle.net/10453/145906