Chromosome Encoding Schemes in Genetic Algorithms for the Flexible Job Shop Scheduling: A State-of-art Review Useful for Artificial Intelligence Applications
Paper
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 |
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Presentation Type | Paper |
Authors | Xuewen, Huang, Islam, Sardar M. N. and Zhou, Yuxun |
Journal or Proceedings Title | Proceedings of the 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) |
Number of Pages | 8 |
Year | 2020 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISBN | 9781728194370 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CITISIA50690.2020.9371789 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/9371789 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/9371766/proceeding |
Conference/Event | 2020 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. |
Keywords | Chromosome coding method; Flexible job shop scheduling; Genetic algorithm |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Dalian University of Technology, China |
Victoria University | |
School of Business |
https://research.usq.edu.au/item/v85xv/chromosome-encoding-schemes-in-genetic-algorithms-for-the-flexible-job-shop-scheduling-a-state-of-art-review-useful-for-artificial-intelligence-applications
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