About TTGS

Smart Scheduling for
Modern Institutions

TTGS was built to eliminate the hours of manual effort spent on timetable planning. We combine a clean administrative interface with a powerful Genetic Algorithm engine to deliver conflict-free schedules in seconds.

Built to save institutions time and effort

Scheduling a timetable for an entire college or university is one of the most complex administrative tasks — involving hundreds of constraints, rooms, teachers, and time slots. TTGS makes it effortless.

TTGS started as a project to tackle a real-world problem faced by academic institutions: the enormous time and effort required to build a workable timetable. Coordinators and administrators would spend days manually arranging schedules, only to discover conflicts that required starting over.

"Our goal is to give every educational institution — regardless of size — access to intelligent, automated scheduling that just works."

Today, TTGS handles teachers, rooms, meeting times, courses, departments, and sections through a straightforward dashboard. A single click triggers the genetic algorithm, and within seconds, a fully optimised, conflict-free timetable is ready to download.

Our core principles

Speed

Generate complete, optimised timetables in seconds rather than days of manual work.

Reliability

Every generated schedule is verified to be free of instructor and room conflicts before delivery.

Simplicity

A clean, step-by-step interface means no technical expertise is needed to manage your timetable.

Scalability

Works equally well for small colleges and large multi-department universities with many sections.

How the Genetic Algorithm works

TTGS uses an evolutionary approach inspired by natural selection to find the optimal schedule from a vast search space of possibilities.

1
Initialise Population

A set of random timetable schedules is created as the starting population, each representing a possible assignment of courses, rooms, instructors, and times.

2
Evaluate Fitness

Each schedule is scored based on the number of conflicts — clashing rooms, double-booked instructors, or capacity violations. Fewer conflicts means higher fitness.

3
Selection & Crossover

The fittest schedules are selected via tournament selection and combined to produce new offspring schedules, inheriting the best traits from each parent.

4
Mutation & Repeat

Random mutations introduce variation to avoid local optima. The process repeats until a zero-conflict schedule is found or the generation limit is reached.

Algorithm At a Glance

Key parameters that drive TTGS's scheduling engine and ensure convergence toward a conflict-free solution.

9
Population Size
5%
Mutation Rate
3
Tournament Size
Conflict Checks
Room capacity vs. class size
Instructor double-booking
Room double-booking
Section scheduling overlap

Everything in one account

Secure Admin Account

Register for a free account to access the full dashboard. Your data is protected with Django's authentication system.

Create Account
One-Click Generation

Once your data is entered, generating a full conflict-free timetable takes a single click. The algorithm runs on the server — no setup required.

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Terms & Conditions

Read our terms of service to understand the usage policy, data handling, and responsibilities when using TTGS.

Read Terms

Ready to simplify your scheduling?

Create a free account and generate your first conflict-free timetable today.

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