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Mathematical model and simulated annealing algorithm for Chinese high school timetabling problems under the new curriculum innovation

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Abstract

As the first attempt, this paper proposes a model for the Chinese high school timetabling problems (CHSTPs) under the new curriculum innovation which was launched in 2014 by the Chinese government. According to the new curriculum innovation, students in high school can choose subjects that they are interested in instead of being forced to select one of the two study directions, namely, Science and Liberal Arts. Meanwhile, they also need to attend compulsory subjects as traditions. CHSTPs are student-oriented and involve more student constraints that make them more complex than the typical “Class-Teacher model”, in which the element “Teacher” is the primary constraint. In this paper, we first describe in detail the mathematical model of CHSTPs and then design a new two-part representation for the candidate solution. Based on the new representation, we adopt a two-phase simulated annealing (SA) algorithm to solve CHSTPs. A total number of 45 synthetic instances with different amounts of classes, teachers, and levels of student constraints are generated and used to illustrate the characteristics of the CHSTP model and the effectiveness of the designed representation and algorithm. Finally, we apply the proposed model, the designed two-part representation and the two-phase SA on10 real high schools.

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Acknowledgements

This work was supported in part by the Outstanding Young Scholar Program of National Natural Science Foundation of China (NSFC) (Grant No. 61522311), in part by the General Program of NSFC (Grant No. 61773300), in part by the Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China (2017JZ017), and in part by the Doctoral Students’ Short-Term Study Abroad Scholarship Fund of Xidian University.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Xingxing Hao, Jing Liu, Yutong Zhang & Gustaph Sanga

Authors
  1. Xingxing Hao
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  2. Jing Liu
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Correspondence to Jing Liu.

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Xingxing Hao received the BS degree in intelligent science and technology from Xidian University, China in 2014. Now, he is pursuing the PhD degree in circuits and systems from the School of Artificial Intelligence, Xidian University, China. His research interests include combinatorial optimization, evolutionary algorithms, evolutionary multitasking, and hyper-heuristics.

Jing Liu received the BS degree in computer science and technology and the PhD degree in circuits and systems from Xidian University, China in 2000 and 2004, respectively. In 2005, she joined Xidian University as a lecture, and was promoted to a full professor in 2009. From Apr. 2007 to Apr. 2008, she worked at the University of Queensland, Australia as a postdoctoral research fellow, and from Jul. 2009 to Jul. 2011, she worked at the University of New South Walesat the Australian Defence Force Academy as a research associate. Now, she is a full professor in School of Artificial Intelligence, Xidian University. Her research interests include evolutionary computation, complex networks, fuzzy cognitive maps, multiagent systems, and data mining.

Yutong Zhang received the BS degree in intelligent science and technology from Xidian University, China in 2014. Now, he is pursuing the PhD degree in circuits and systems from the School of Artificial Intelligence, Xidian University, China. His research interests include transportation problems and evolutionary algorithms.

Gustaph Sanga received BS degree in Computer Science from the University of Dar es Salaam, Tanzania. He received his Master of Engineering in Computer Science and Technology from Hunan University, China. Now he is a PhD student with the School of Artificial Intelligence, Xidian University, China. His research interest is focused on evolutionary computations, complex networks, artificial immune systems, sematic web technology, big data, and data science.

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Mathematical model and simulated annealing algorithm for Chinese high school timetabling problems under the new curriculum innovation

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Mathematical model and simulated annealing algorithm for Chinese high school timetabling problems under the new curriculum innovation

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Hao, X., Liu, J., Zhang, Y. et al. Mathematical model and simulated annealing algorithm for Chinese high school timetabling problems under the new curriculum innovation. Front. Comput. Sci. 15, 151309 (2021). https://doi.org/10.1007/s11704-020-9102-4

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