SlotManager: a microcomputer-based decision support system for university timetabling
Decision Support Systems
Building Effective Decision Support Systems
Building Effective Decision Support Systems
A Survey of Automated Timetabling
Artificial Intelligence Review
Generating Complete University Timetables by Combining Tabu Search with Constraint Logic
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
A Comparison of Annealing Techniques for Academic Course Scheduling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
An effective hybrid algorithm for university course timetabling
Journal of Scheduling
A MULTI-AGENT SYSTEM FOR UNIVERSITY COURSE TIMETABLING
Applied Artificial Intelligence
Ant algorithms for the university course timetabling problem with regard to the state-of-the-art
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Distributed choice function hyper-heuristics for timetabling and scheduling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Local search techniques for large high school timetabling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Each October, the Executive Education Unit at the Universidad de Chile develops its course schedules for the following year. By 2008, the complexities of increasing enrollments and course offerings had rendered its manual timetabling process unmanageable. Inconvenient and inflexible scheduling decisions were causing discontent among instructors and students, making the need for a more efficient system of assigning classrooms patent. Three characteristics distinguish the unit's situation from the classic university course timetabling problem. First, its courses vary in duration, ranging between 15 and 30 weeks. Second, its course start dates are spread over the academic year. Finally, each course's start date is flexible and must fall within a window defined by the earliest and latest start dates. This paper presents an automated computational system that generates optimal timetables and classroom assignments for all the unit's courses, minimizing both operating costs and schedule conflicts. When we compared the schedules it generated with the unit's manually generated timetables, we found that our system yielded average cost savings of 35 percent; in addition, it reduced execution times (for generating schedules) from two weeks to less than 30 minutes.