Analysis and Solution of University Examination Arrangement Problems
In order to give the best examination arrangement, we first analyzed the relevant factors and set up the model. Meanwhile, we calculated the probability of conflict arising from the random arrangements. Later, in order to better select the time period of the subject arrangement and evaluate the results, we established the local conflict function and the global benefit function. In addition, we used the dye matching algorithm and the genetic algorithm to solve it. Finally, we provided ideas for solving this problem with other intelligent algorithms.
KeywordsExamination arrangement Modeling Benefit function Dyeing matching algorithm Genetic algorithm
In the process of research, we should especially thank Mr. Zhang for his guidance and supervision. Without his help, we can’t understand the pattern of the exam arrangement in our school. It will be hard for us to stick, and apply our model to reality. At the same time, we are grateful to become each other’s partners. We read the related literature or books together, and discuss for the model and algorithms. Besides, thank you to our school for providing us with a good learning environment.
- 1.Shasha, Y.: Research on resource conflict optimization algorithm for college examination system. Wirel. Internet Technol. 03, 66–68 (2016). (in Chinese)Google Scholar
- 2.Li, C.: An improved graph algorithm for the examination of college entrance examination under the credit system environment. Appl. Comput. 24(03), 220–225 (2015). (in Chinese)Google Scholar
- 3.Long, H., Tan, C.: Requisition system based on genetic algorithm. Appl. Comput. Syst. 23(01), 184–187 (2014). (in Chinese)Google Scholar
- 4.Deyan, W.: Optimization of dynamic test arrangements algorithm. Wuxi Voc. Tech. Coll. 12(06), 14–16 (2013). (in Chinese)Google Scholar
- 5.Deyan, W.: Study on dynamic test arrangement based on particle swarm optimization. Digit. Commun. 40(05), 11–13 (2013). (in Chinese)Google Scholar
- 6.Wen, L.: Design and implementation of college course arranging system based on genetic algorithm. University of Electronic Science and Technology of China (2012)Google Scholar
- 7.Niu, Y., Yan, G., Xie, G., Xie, K.: Research on genetic algorithms based on knowledge. J. Taiyuan Univ. Technol. 42(02), 121–125 (2011). (in Chinese)Google Scholar
- 8.Cai, M.: Design and implementation of automatic college test-taking algorithm. Comput. Eng. Appl. 46(24), 69–72 (2010). (in Chinese)Google Scholar
- 9.Yong, O., Tao, L.: Design and implementation of college automatic examination system. J. Hubei Univ. Technol. 24(04), 67–70 (2009). (in Chinese)Google Scholar
- 10.Ling, T.: Design and implementation of university automatic test engine. Comput. Eng. Des. 10, 2443–2445 (2007). (in Chinese)Google Scholar
- 11.Qing, W., Yawen, Z., Wei, Z.: Staining-matching algorithm for college examination papers. J. Univ. Shanghai Sci. Technol. 02, 157–161 (2005). (in Chinese)Google Scholar
- 14.Wang, T., Xia, Y., Muppala, J., Hamdi, M.: Achieving energy efficiency in data centers using an artificial intelligence abstraction model. IEEE Trans. Cloud Comput. (2015)Google Scholar