A Genetic Algorithm Academic Scheduling Engine

A Genetic Algorithm Academic Scheduling Engine is a Python-based scheduler that uses evolutionary optimization to generate high-quality academic timetables. It handles real-world constraints such as room capacities, facilitator preferences, instructor load, time-slot conflicts, and special SLA course rules. Using a genetic algorithm, it evolves candidate schedules over multiple generations to find the optimal arrangement.

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Features

• Genetic Algorithm Optimization
  Uses selection, multi-point crossover, and adaptive mutation to iteratively improve schedules.

• Constraint-Aware Fitness Function
  Evaluates schedules based on room capacity, facilitator preferences, workload balance, and SLA rules.

• Time-Conflict Management
  Handles overlapping time slots, consecutive scheduling penalties, and SLA-specific timing constraints.

• Facilitator Load Balancing
  Penalizes overloading facilitators while rewarding optimal assignments.

• Customizable Inputs
  Easily define activities, facilitators, rooms, and time slots.

• Exportable Results
  Produces a neatly formatted best_schedule.txt file with the final optimized schedule.

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Installation

1. Clone the repository:

git clone https://github.com/AbdalrahmanBashir/genetic-algorithm-scheduler.git
cd genetic-algorithm-scheduler

2. Install dependencies (Python 3.8+ required):

pip install numpy

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Usage

1. Edit main.py to define your activities, rooms, facilitators, and time slots.
2. Run the scheduling engine:

python main.py

3. The best schedule will be saved as best_schedule.txt. The console will also print fitness improvements per generation.

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How It Works

1. Initial Population: Random schedules are generated.
2. Fitness Calculation: Each schedule is scored based on constraints and penalties.
3. Selection: Top schedules are probabilistically chosen using softmax on fitness scores.
4. Crossover & Mutation: Selected schedules are combined and mutated to create new candidates.
5. Iteration: Steps 2–4 repeat for a defined number of generations.
6. Output: The highest-fitness schedule is saved and presented in a human-readable format.

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Example Output

Best Schedule is:
Fitness Score: 3.75000

Activity      Facilitator          Room            Time Slot            Enrollment
--------------------------------------------------------------------------
SLA100A       Glen                 Slater 003      10                  50
SLA100B       Banks                Roman 201       11                  50
SLA191A       Tyler                Beach 301       12                  50
...

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Why Use This Engine

Scheduling academic courses is a complex, NP-hard problem. This engine demonstrates how genetic algorithms can produce high-quality, near-optimal schedules efficiently, without brute-force search. It’s a practical solution for:

• Universities and colleges
• Training programs
• Academic event scheduling
• Research into evolutionary optimization

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License

This project is licensed under the MIT License.

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