Application of Selection Sequence Optimization Algorithm to University Timetabling Problem
A.P. Dimitriev, T.A. Lavina, A.H. Aleksandrov
Available Online 13 May 2020.
- https://doi.org/10.2991/assehr.k.200509.100How to use a DOI?
- timetabling, timetable classes, simulated annealing, discrete optimization, optimization algorithm, objective function
- The object of study is the automated scheduling and optimization of class timetables. To study the operation of various discrete optimization algorithms applied to the timetabling problem, as well as new algorithms a mathematical model of schedules is used. For this model, the so-called selection sequence optimization algorithm is characterized by the best indicators for the quality of the resulting timetable. The paper presents a modification of this algorithm based on the multiple start method. The adaptation of the selection sequence optimization algorithm directly to the timetabling problem at the university is proposed. The components of the objective function for optimization of timetable, taking into account psycho-pedagogical requirements to the educational process are considered. Such characteristic of the quality of timetable as the number of unplaced man-hours is highlighted. The results of optimization using the above algorithm and the random search method are compared in terms of time and quality of the obtained timetable. Numerical values of the parameters of the above algorithm are proposed.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - A.P. Dimitriev AU - T.A. Lavina AU - A.H. Aleksandrov PY - 2020 DA - 2020/05/13 TI - Application of Selection Sequence Optimization Algorithm to University Timetabling Problem BT - International Scientific Conference “Digitalization of Education: History, Trends and Prospects” (DETP 2020) PB - Atlantis Press SP - 549 EP - 555 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200509.100 DO - https://doi.org/10.2991/assehr.k.200509.100 ID - Dimitriev2020 ER -