TY - GEN
T1 - Autonomous generation of conflict-free examination timetable using constraint satisfaction modelling
AU - Elsaka, Tarek
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/30
Y1 - 2017/10/30
N2 - Examination timetable (ETT) is a complex administrative task at educational institutions that must fulfill various constraints to generate the ETT to schedule exam sessions within a precise period. The ETT problem could be modeled as Constraint Satisfaction Problems (CSPs). In addition, it could be particularly investigated by Constraint Logic Programming (CLP) approach. This paper uses a real examination dataset from the Community College (CC), University of Sharjah (UoS). This dataset has very rich data such as the large number of student enrolments related to many departments, an accumulative number of combined courses, low number of exam halls, very limited timeslots, low number of invigilators and distant campuses. This dataset has many practical constraints to be satisfied such as a course taught at many campuses must has the same exam date and an invigilator can invigilate at any campus. This paper applies the CSP definitions as well as the Optimization Programming Language (OPL) to model the ETT dataset and automatically generate a conflict-free ETT solution using a CLP Solver. Finally, it uses the results to satisfy the proposed constraints in the model.
AB - Examination timetable (ETT) is a complex administrative task at educational institutions that must fulfill various constraints to generate the ETT to schedule exam sessions within a precise period. The ETT problem could be modeled as Constraint Satisfaction Problems (CSPs). In addition, it could be particularly investigated by Constraint Logic Programming (CLP) approach. This paper uses a real examination dataset from the Community College (CC), University of Sharjah (UoS). This dataset has very rich data such as the large number of student enrolments related to many departments, an accumulative number of combined courses, low number of exam halls, very limited timeslots, low number of invigilators and distant campuses. This dataset has many practical constraints to be satisfied such as a course taught at many campuses must has the same exam date and an invigilator can invigilate at any campus. This paper applies the CSP definitions as well as the Optimization Programming Language (OPL) to model the ETT dataset and automatically generate a conflict-free ETT solution using a CLP Solver. Finally, it uses the results to satisfy the proposed constraints in the model.
KW - Constraint logic programming
KW - Constraint satisfaction
KW - Optimization programming language
KW - Planning
UR - https://www.scopus.com/pages/publications/85039897013
UR - https://www.scopus.com/inward/citedby.url?scp=85039897013&partnerID=8YFLogxK
U2 - 10.1109/IDAP.2017.8090236
DO - 10.1109/IDAP.2017.8090236
M3 - Conference contribution
AN - SCOPUS:85039897013
T3 - IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
BT - IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017
Y2 - 16 September 2017 through 17 September 2017
ER -