Intelligent Timetable & Workload Manager School of Management — demo dataset

Department data

Everything the scheduler reasons about. Edit the numbers and the next run uses them — or import your own CSV.

Teachers

Caps are the hard limits the scheduler must respect.

Rooms

Capacity and type decide which sessions can go where.

Class groups

Subjects

Each subject's weekly hours become individual sessions to place. Labs are placed as contiguous blocks.

Presets

The same department under three different pressures. This is the quickest way to see what constraint tightness does to a search.

The grid

Shrinking the timetable is the single strongest lever on difficulty.

Rooms in play

Untick a room to take it out of service.

Hard constraints

Never violated. A timetable breaking any of these is not a timetable — the verifier re-checks all of them independently after every run.

Soft constraints

Traded off against each other. The weight is how many penalty points one unit of that problem costs; local search minimises the weighted total.

Generate the timetable

Backtracking search with most-constrained-session-first ordering and randomised restarts, then local search on the soft score.

Same seed, same timetable.

SEARCH LOG

Not run yet.

WHAT BLOCKED PLACEMENTS

Run the search to see which rules did the work.

SOFT SCORE BREAKDOWN

Generate a timetable first.

Teacher workload

Weekly periods against each teacher's cap, and how the load falls across the week. The marker on each bar is that teacher's cap.

Generate a timetable first.

Room utilisation

Generate a timetable first.