Chromosome Encoding Schemes in Genetic Algorithms for the Flexible Job Shop Scheduling: A State-of-art Review Useful for Artificial Intelligence Applications

Paper


Xuewen, Huang, Islam, Sardar M. N. and Zhou, Yuxun. 2020. "Chromosome Encoding Schemes in Genetic Algorithms for the Flexible Job Shop Scheduling: A State-of-art Review Useful for Artificial Intelligence Applications." 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA). Australia 25 - 27 Nov 2020 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/CITISIA50690.2020.9371789
Paper/Presentation Title

Chromosome Encoding Schemes in Genetic Algorithms for the Flexible Job Shop Scheduling: A State-of-art Review Useful for Artificial Intelligence Applications

Presentation TypePaper
AuthorsXuewen, Huang, Islam, Sardar M. N. and Zhou, Yuxun
Journal or Proceedings TitleProceedings of the 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)
Number of Pages8
Year2020
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISBN9781728194370
Digital Object Identifier (DOI)https://doi.org/10.1109/CITISIA50690.2020.9371789
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9371789
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9371766/proceeding
Conference/Event2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)
Event Details
2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)
Delivery
Online
Event Date
25 to end of 27 Nov 2020
Event Location
Australia
Abstract

This paper undertakes an innovative review and organization of the relevant issues of the FJSP in the genetic algorithm to provide some systematic way of organizing its issues and provide useful insights in this method of the genetic algorithm Flexible Job-shop Scheduling Problem (FJSP) is a type of scheduling problem with a wide range of application backgrounds. In recent years, genetic algorithms have become one of the most popular algorithms for solving FJSP problems and have attracted widespread attention. In this paper, a comprehensive review of chromosome coding methods of the genetic algorithm for solving the FJSP and three standards are used to compare the advantages and disadvantages of each coding method. The results show that MSOS-I coding is a better chromosomal encoding method for solving FJSP problems, whose chromosome structure is simple, feasibility and larger storage. The main contribution of this paper is to fill the literature gap, because No such comprehensive review of the FJSP in the GA prevails in the existing literature. This comprehensive review will be useful for scholars and practical applications of the FJSP and the genetic algorithm for artificial intelligence and machine learning implementations and applications.

KeywordsChromosome coding method; Flexible job shop scheduling; Genetic algorithm
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460299. Artificial intelligence not elsewhere classified
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Byline AffiliationsDalian University of Technology, China
Victoria University
School of Business
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