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2022 | OriginalPaper | Buchkapitel

16. An QUasi-Affine TRansformation Evolution (QUATRE) Algorithm for Job-Shop Scheduling Problem by Mixing Different Strategies

verfasst von : Qing-Yong Yang, Shu-Chuan Chu, Chien-Ming Chen, Jeng-Shyang Pan

Erschienen in: Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Verlag: Springer Singapore

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Abstract

How to solve the Job-Shop Scheduling problem (JSP) effectively and make the most efficient use of resources has always been the focus of academic and engineering circles. Aiming at the traditional JSP problem, this paper proposes a new QUasi-Affine Transformation Evolution algorithm (QUATRE) to solve it, called QUATRE-SAO for short. The QUATRE-SAO algorithm combines Simulated Annealing (SA) strategy and Opposition-based Learning (OBL) strategy to enhance the algorithm to jump out of local optimum and further improve the optimization performance of the algorithm. Through the comparative experiment of FT and LA series standard test examples, the results show that the QUATRE-SAO algorithm can solve the JSP problem better and can get a better solution.

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Titel: An QUasi-Affine TRansformation Evolution (QUATRE) Algorithm for Job-Shop Scheduling Problem by Mixing Different Strategies
verfasst von: Qing-Yong Yang
Shu-Chuan Chu
Chien-Ming Chen
Jeng-Shyang Pan
Verlag: Springer Singapore
Buch: Advances in Smart Vehicular Technology, Transportation, Communication and Applications
Print ISBN: 978-981-16-4038-4

Electronic ISBN: 978-981-16-4039-1

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-981-16-4039-1_16

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