Probabilistic Curriculum-based Examination Timetabling

Beitrag bei einer Tagung
(Originalarbeit)


Details zur Publikation

Autor(en): Bassimir B, Wanka R
Herausgeber: Edmund K. Burke, Luca Di Gaspero, Barry McCollum, Nysret Musliu, Ender Özcan
Jahr der Veröffentlichung: 2018
Tagungsband: Proc 12th International Conference on the Practice and Theory of Automated Timetabling (PATAT)
Seitenbereich: 273-285
ISBN: 978-0-9929984-2-4
Sprache: Englisch


Abstract

In the literature, the examination timetabling problem (ETTP) is mostly described as a post enrollment problem (PE-ETTP). As such, it is known at optimization time how many students will take an exam and consequently how big a room is needed and which exams should not be held at the same time because of overlapping student lists. To compute a timetable using this approach, students need to register for exams before the timetable is generated. A direct consequence is that at registration time students have no idea when their exams are being held. Furthermore as timetables are often released at the end of the semester, it is hard for lecturers to plan their other responsibilities accordingly. This leads to a negative reaction from both the student body and the staff holding the exams. In this paper, we describe a curriculum-based examination timetabling variant that is similar to the curriculum-based examination timetabling problem model (CB-ETTP) introduced by Cataldo et al. 2017. The aim of the model introduced in this work is to combine the positive aspects of PE-ETTP and CB-ETTP by the use of machine learning while reducing the problems of the CB-ETTP, namely the overestimation in the number of students taking an exam. We describe an approach to calculate the number of students taking an exam by using old planning data. Furthermore we give an example for integrating the knowledge from past experience as a new soft constraint. Through the addition of this new soft constraint, we get a measure for the robustness of the timetable in respect to the uncertainty in the data. Finally, we present experiments based on real world data from the University of Erlangen-Nuremberg (FAU) showing that the approach gives a good estimation for the number of students
with only slight deviations from the actual numbers.


FAU-Autoren / FAU-Herausgeber

Bassimir, Bernd
Professur für Informatik (Effiziente Algorithmen und Kombinatorische Optimierung)
Wanka, Rolf Prof. Dr.
Professur für Informatik (Effiziente Algorithmen und Kombinatorische Optimierung)


Zitierweisen

APA:
Bassimir, B., & Wanka, R. (2018). Probabilistic Curriculum-based Examination Timetabling. In Edmund K. Burke, Luca Di Gaspero, Barry McCollum, Nysret Musliu, Ender Özcan (Eds.), Proc 12th International Conference on the Practice and Theory of Automated Timetabling (PATAT) (pp. 273-285). Vienna, AT.

MLA:
Bassimir, Bernd, and Rolf Wanka. "Probabilistic Curriculum-based Examination Timetabling." Proceedings of the 12th International Conference on the Practice and Theory of Automated Timetabling (PATAT), Vienna Ed. Edmund K. Burke, Luca Di Gaspero, Barry McCollum, Nysret Musliu, Ender Özcan, 2018. 273-285.

BibTeX: 

Zuletzt aktualisiert 2018-28-09 um 13:38