Look-ahead techniques for micro-opportunistic job shop scheduling
Look-ahead techniques for micro-opportunistic job shop scheduling
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
A constraint-based approach to high-school timetabling problems: a case study
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
TESS: an interactive support system for school timetabling
ITEM '96 Proceedings of the IFIP TC3/WG 3.4 international conference on Information technology in educational management for the schools of the future
A Survey of Automated Timetabling
Artificial Intelligence Review
Improving a Heuristic Repair Method for Large-Scale School Timetabling Problems
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Combining local search and look-ahead for scheduling and constraint satisfaction problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
| Hi-index | 0.01 |
School timetabling has been one of the most difficult scheduling problems for decades. Scarce teaching resources and the need for supporting stringent teaching requirements such as co-teaching, alternate-week lesson and split-class teaching complicates the problem even more. In this paper, a sophisticated automated school timetabling system called @PT is described. @PT makes use of a number of intelligent techniques including constraint technology, heuristics, local search operators, and tabu-list like data structure to tackle the complicated problem. The system also adjusts its search behavior dynamically at run-time. @PT is equipped with a full range of functions to facilitate the whole timetabling process from data entry to report generation. The system is robust and manages to solve real problem instances effectively on an ordinary personal computer within minute.