TY - CHAP
T1 - A Two-Phase Constraint Programming Model for Examination Timetabling at University College Cork
AU - Genc, Begum
AU - O’Sullivan, Barry
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Examination timetabling is a widely studied NP-hard problem. An additional challenge to the complexity of the problem are many real-world requirements that can often prevent the relaxation of some constraints. We report on a project focused on automating the examination timetabling process of University College Cork (UCC) to enhance the examination schedules so that they are fairer to student, as well as being less resource intensive to generate from an administrative point of view. We work with a formulation developed in collaboration with the institution and real data that it provided to us. We propose a two-phase constraint programming approach to solving UCC ’s examination timetabling problem. The first phase considers the timing of examinations while the second phase considers their allocation to rooms. Both phases are modelled using bin-packing constraints and, in particular, an interesting variant in which items can be split across multiple bins. This variant is known as bin packing with fragmentable items. We investigate the tightly linked constraints and difficulties in decomposing the centralised model. We provide empirical results using different search strategies, and compare the quality of our solution with the existing UCC schedule. Constraint programming allows us to easily modify the model to express additional constraints or remove the pre-existing ones. Our approach generates significantly better timetables for the university, as measured using a variety of real-world quality metrics, than those prepared by their timetabling experts, and in a reasonable timeframe.
AB - Examination timetabling is a widely studied NP-hard problem. An additional challenge to the complexity of the problem are many real-world requirements that can often prevent the relaxation of some constraints. We report on a project focused on automating the examination timetabling process of University College Cork (UCC) to enhance the examination schedules so that they are fairer to student, as well as being less resource intensive to generate from an administrative point of view. We work with a formulation developed in collaboration with the institution and real data that it provided to us. We propose a two-phase constraint programming approach to solving UCC ’s examination timetabling problem. The first phase considers the timing of examinations while the second phase considers their allocation to rooms. Both phases are modelled using bin-packing constraints and, in particular, an interesting variant in which items can be split across multiple bins. This variant is known as bin packing with fragmentable items. We investigate the tightly linked constraints and difficulties in decomposing the centralised model. We provide empirical results using different search strategies, and compare the quality of our solution with the existing UCC schedule. Constraint programming allows us to easily modify the model to express additional constraints or remove the pre-existing ones. Our approach generates significantly better timetables for the university, as measured using a variety of real-world quality metrics, than those prepared by their timetabling experts, and in a reasonable timeframe.
UR - https://www.scopus.com/pages/publications/85091304312
U2 - 10.1007/978-3-030-58475-7_42
DO - 10.1007/978-3-030-58475-7_42
M3 - Chapter
AN - SCOPUS:85091304312
SN - 9783030584740
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 724
EP - 742
BT - Principles and Practice of Constraint Programming - 26th International Conference, CP 2020, Proceedings
A2 - Simonis, Helmut
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Principles and Practice of Constraint Programming, CP 2020
Y2 - 7 September 2020 through 11 September 2020
ER -