Planifica turns school timetabling from a multi-week manual ordeal into a guided, repeatable workflow. Instead of juggling spreadsheets and hoping the pieces fit, you describe your school as a set of constraints — teacher availability, room capacity, weekly subject hours, break rules, and pedagogical preferences — and a constraint-optimization engine searches millions of possible arrangements to find one that respects every hard rule at once.
The approach is deterministic and verifiable rather than a black box. Hard constraints are always satisfied, conflicts are eliminated by construction, and you can re-run the optimization at any time and get the same guarantees. This page walks through exactly what happens at each stage, who the workflow is built for, and the questions schools ask most before adopting it.
Enter the building blocks of your school: grade levels and groups, subjects and their weekly hours, teachers and their availability, and the rooms or labs each subject needs. You also set the rules that make a timetable usable in practice — maximum daily hours, mandatory breaks, spacing between difficult subjects, and any slots that must stay fixed. Everything you enter becomes a constraint the engine must honour.
Launch the engine. It explores an enormous space of possible timetables, discarding any arrangement that breaks a hard constraint and scoring the rest against your soft preferences — balanced teacher workloads, well-placed core subjects, minimal gaps. Initial results are typically ready in minutes rather than the days or weeks a manual process demands, because the search is done for you instead of by hand.
Examine the proposed timetable in a clear weekly grid. Because the engine reports which constraints are satisfied, you can trust that no teacher is double-booked and no room is over capacity. If you want to change a preference — move a subject earlier, free a teacher's afternoon, protect a study period — adjust the constraint and re-run. The engine keeps disruption minimal, so small changes rarely force a full rebuild.
Once the timetable is right, publish it and share the views each audience needs — per grade, per teacher, or per room. When something changes mid-year (a new teacher, an unavailable room, a curriculum update), you feed the change back in and re-run, keeping the same guarantees. The workflow is repeatable, so every semester or program change is a re-run rather than a fresh start.
School administrators and directors of studies who own the master timetable get the biggest gain: what used to be weeks of trial and error becomes a configured model they can re-run whenever reality shifts. Coordinators responsible for a single grade or department can model their slice precisely — specialised labs, part-time staff, shared rooms — without breaking the wider schedule.
The workflow suits schools of every size, from a single campus of a few hundred students to networks running thousands across multiple sites. It is especially valuable where constraints are dense and change often: rotating schedules, block timetables, limited specialised rooms, or frequent mid-year adjustments. If your timetable has to satisfy many rules at once and be defensible to teachers and families, this is the workflow built for it.
Most of the time is the one-off setup of your constraints. Once your school is modelled, an optimization run typically returns a complete timetable in minutes, and later re-runs after a change are faster still because the model already exists.
If your constraints conflict — more required hours than available slots, for example — the engine tells you which rules cannot all be met at once instead of silently producing a broken schedule. You then relax a soft preference or adjust a resource and re-run, with full visibility into the trade-off.
Yes. Any slot you lock — a standing staff meeting, a shared facility booking, a fixed exam period — is treated as a hard constraint the engine schedules everything else around, so your non-negotiables are preserved through every re-run.