A constructive tool to predict timetable feasibility under user defined constraints.

Discussions on housing issues in higher education institutions and their impact on timetables are seldom founded in data. Insight in the interaction between resource levels in educational logistics, scheduling policies and timetables is limited. We developed a tool show these interactions. The maximum utilisation to create feasible timetables is found through automated timetabling. Student, staff and management preferences are joined in a penalty structure that refelects the quality of a timetable. By scheduling the same set of events in various sets of rooms with a simulated annealing heuristic we can identify the point at which it becomes impossible to create a complete timetable under the given penalty structure. Adjusting the penalty structure enables us to compare scenarios, determine minimum resource levels or predict if feasible timetables can be found under the given constraints, resource levels and demand for educational space.