Closed days or periods can reduce candidate domains for multiple classes at once.
Constraint-based learning timetable
Automatic school timetables that are measurable, transparent, and auditable.
RuangJadwal helps curriculum teams build timetable drafts from real school data, assess infeasibility risk before generation, and present evidence so corrective actions are not based on unsupported assumptions.
Designed to combine operational school data, constraint validation, and traceable draft generation.
20+readiness and academic data conflict checks
Dynamicrisk diagnosis when rules and loads change
Auditableinfeasibility evidence for corrective action
Information and account activation
Need complete information or account activation?
Prospective customers can contact the iGuru team via WhatsApp or email to request product information, onboarding guidance, and account activation.
Mention your school name and activation need so the team can respond accurately.Real school constraints
Timetabling is more than placing subjects into a grid.
A timetable must align teaching loads, teacher availability, hour distribution, classes, rooms, and operational priorities. A small rule change can close many candidate slots and make the overall combination infeasible.
Weekly periods, grade-specific subjects, and teacher assignments must remain consistent.
Rules that are reasonable in isolation may become infeasible when combined.
Operators need to know which teacher, day, period, subject, or class blocks progress.
Why it is worth using
Built for curriculum teams that need speed and evidence.
Pre-generation validation
The system checks data readiness, constraint pressure, and availability risk before the solver runs.
Risk root-cause diagnosis
Risk panels highlight factors that are most likely to make the timetable difficult or infeasible.
Integrated academic data
Teachers, classes, rooms, subjects, assignments, loads, days, and periods are managed in one workspace.
Reviewable drafts
Generated drafts can be inspected, adjusted, exported, and discussed internally.
Scientific basis
Scientific research publications applied in this application.
Timetabling research describes school scheduling as a combinatorial problem with many constraints. The application therefore applies constraint modeling, exact optimization, decomposition, infeasibility diagnosis, and repair strategies grounded in documented scientific work.
This publication summarizes educational timetabling problems, benchmarks, hard and soft constraints, and state-of-the-art results.
Applied in the application
Open source Applied as the basis for separating hard constraints, soft quality, benchmarks, and solution-quality evaluation in Schedule Studio.
This article shows that timetabling can be modeled as exact optimization over time, room, class, and constraint decisions.
Applied in the application
Open source Applied in candidate-placement variables, integer/Boolean model auditing, and independent solver-output validation.
This article emphasizes school timetable quality, including teacher idle time, workload restrictions, and school-specific constraint diversity.
Applied in the application
Open source Applied to quality objectives, constraint-pressure diagnosis, and teacher workload/availability controls before draft generation.
This publication shows the relevance of adaptive large-neighborhood search, destroy-repair, and repair strategies for high-school timetabling.
Applied in the application
Open source Applied as guidance for portfolio search, warm starts, repair, and solution improvement when a monolithic model struggles to find feasibility.
Timetabling is addressed through operations research and artificial intelligence techniques, including constraint satisfaction, tabu search, simulated annealing, and genetic algorithms.
Open sourceAutomated timetabling research emphasizes methods applicable across scheduling domains with diverse constraints.
Open sourceWorkflow
From school data to a feasible draft timetable.
Start from Dapodik data or manual input mapped into academic master data.
Set teacher availability, teaching loads, eligible classes, days, and lesson periods.
Review constraint pressure and improvement suggestions before draft generation.
Build drafts, inspect solver evidence, and export timetables for internal validation.
Prepare school timetables in a more measurable way.
Use RuangJadwal to accelerate timetable-draft preparation and reduce decisions based on unsupported assumptions.