Scheduling by NSGA-II: Review and Bibliometric Analysis release_p37agkf6pzegbmwategpmobtfq

by Iman Rahimi, Amir Gandomi, Kalyanmoy Deb, Fang Chen, Mohammad Reza Nikoo

Published in Processes by MDPI AG.

2022   Volume 10, p98


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.
In application/xml+jats format

Archived Files and Locations

application/pdf  8.8 MB
file_oxaexdjzhrblla4sw7qqn7jrlu (publisher) (webarchive)

Web Captures
2022-06-23 07:54:59 | 71 resources
webcapture_6iuhnai4zrck5nwm6jwczvwuwe (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-01-04
Language   en ?
Container Metadata
Open Access Publication
In Keepers Registry
ISSN-L:  2227-9717
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 7be81ba3-2aa6-4586-a27d-d9232e1dabbb