OPTIMIZATION OF TIMETABLE AT THE UNIVERSITY

Authors

  • A. I. Kosolap Ukrainian State University of Chemical Technology, Dnipro, Ukraine., Ukraine
  • T. M. Dubovik Ukrainian State ChemicalTechnological University, Dnipro, Ukraine., Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2021-3-15

Keywords:

university timetabling, optimization, problems with boolean variables, exact quadratic regularization method.

Abstract

Context. In this paper, we consider a well-known university scheduling problem. Such tasks are solved several times a year in every educational institution. The problem of constructing an optimal schedule remains open despite numerous studies in this area. This is due to the complexity of the corresponding optimization problem, in particular, its significant dimension. This complicates its numerical solution with existing optimization methods. Scheduling models also need improvement. Thus, schedule optimization is a complex computational problem and requires the development of new methods for solving it. 

Objective. Improvement of optimization models for timetabling at the university and the use of new effective methods to solve them.

Method. We use the exact quadratic regularization method to solve timetabling optimization problems. Exact quadratic regularization allows transforming complex optimization models with Boolean variables into the problem of maximizing the vector norm on a convex set. We use the efficient direct dual interior point method and dichotomy method to solve this problem. This method has shown significantly better results in solving many complex multimodal problems. This is confirmed by many comparative computational experiments. The exact quadratic regularization method is even more effective in solving timetabling problems. This optimization method is used for the first time for this class of problems, so it required the development of adequate algorithmic support.

Results. We propose a new, simpler timetabling optimization model that can be easily implemented software in Excel with the OpenSolver, RoskSolver, and others. We give a small example of building a schedule and describe step-by-step instructions for obtaining the optimal solution. 

Conclusions. An efficient new technology developed for university timetable, which is simple to implement and does not require the development of special software. The efficiency of the technology is ensured by the use of a new method of exact quadratic regularization. 

Author Biographies

A. I. Kosolap, Ukrainian State University of Chemical Technology, Dnipro, Ukraine.

Dr. Sc., Professor, head of the Department of Specialized Computer Systems.

T. M. Dubovik, Ukrainian State ChemicalTechnological University, Dnipro, Ukraine.

Senior Lecturer of the Department of Specialized Computer Systems.

References

de Werra, D. An introduction to timetabling, European Journal of Operational Research, 1985, 19, pp. 151 – 162.

Kosolap A. A new method for global optimization, ESAIM: Proceedings and surveys, 2021, Vol. 71. pp. 121–130.

Junginger W. Timetabling in Germany – a survey [Электронный ресурс], In Interfaces, 1986, Vol. 16, No. 4, pp. 66–74. https://pubsonline.informs.org.

Shraerf A. A survey of automated timetabling, Journal Artificial Intelligence Review, 1999, Volume 13, Issue 2, pp. 1–8.

Oude Vrielink R. A., Jansen E. A., Hans E. W., van Hillegersberg J. Practices in timetabling in higher education institutions: a systematic review. Ann. Oper. Res., 2019, 275, pp. 145–160.

Aziz N. L. A., Aizam N. A. H. A brief review on the features of university course timetabling problem. AIP Conference Proceedings, Volume 2016, Issue 1, pp. 1–8.

Qu R., Burke E.K. A survey of search methodologies and automated system development for examination timetabling. Journal of scheduling, 2009, Volume 12, Number 1, pp. 55–89.

Bettinelli A., Cacchiani V., Roberti R., Toth P. An overview of curriculum-based course timetabling, TOP, 2015, No 23, pp. 313–349. DOI: 10.1007/s11750-015-0366-z.

Hayat Alghamdi, Tahani Alsubait, Hosam Alhakami, Abdullah Baz A Review of Optimization Algorithms for University Timetable Scheduling Engineering, Technology & Applied Science Research, 2020, Vol. 10, No. 6, pp. 6410–6417.

Nocedal J., Wright S. J. Numerical optimization. Springer, 2006. 685 p.

Kosolap A. Practical Global optimization. Dnipro, Publisher Bila K. O., 2020, 192 p.

Published

2021-10-09

How to Cite

Kosolap, A. I., & Dubovik, T. M. (2021). OPTIMIZATION OF TIMETABLE AT THE UNIVERSITY . Radio Electronics, Computer Science, Control, (3), 175–183. https://doi.org/10.15588/1607-3274-2021-3-15

Issue

Section

Control in technical systems