Project Overview

The Automated Timetable Scheduling and Faculty Workload Optimization System is a constraint-driven academic scheduling platform designed to automatically generate conflict-free institutional timetables while optimizing faculty workload distribution. The system enables administrators to manage courses, faculty members, and classroom resources efficiently.

Using a Genetic Algorithm–based optimization approach, the system generates feasible and optimized timetables by satisfying hard institutional constraints while minimizing soft constraint violations related to workload balance and scheduling preferences. This approach ensures both operational correctness and fairness in faculty workload allocation.

---

Features

1. Role-Based Access Control (RBAC): Separate portals for Admin, Faculty, and Students with restricted privileges.
2. Genetic Algorithm–Based Timetable Generation: Automatically generates optimized and conflict-free academic timetables using an evolutionary optimization approach.
3. Hard & Soft Constraint Handling: Supports mandatory constraints (NoFacultyOverlap, NoRoomOverlap, RoomCapacityLimit) and optimizable constraints (BalancedFacultyWorkload, PreferredTimeSlots, GapMinimization).
4. Faculty Workload Optimization: Ensures fair and balanced distribution of teaching hours across faculty members.
5. Dashboard & Analytics: Visual insights into timetable status, faculty workload, and room utilization.

---

Genetic Algorithm

The core of this application is the automated scheduling engine powered by a Genetic Algorithm (GA). This heuristic search algorithm mimics the process of natural selection to find an optimal timetable solution.

1. Chromosome Representation: Each chromosome represents a complete timetable. Each gene corresponds to a timetable slot (course, faculty, room, time).
2. Fitness Function: The fitness score determines how “good” a timetable is, based on:
   • Hard Constraints: Must be satisfied. Examples include NoFacultyOverlap, NoRoomOverlap, and RoomCapacityLimit.
   • Soft Constraints: Desirable but not mandatory. Examples include BalancedFacultyWorkload and PreferredTimeSlots.
3. Initialization: Create a random population of NTimetables.
4. Selection: High-fitness chromosomes are selected using TournamentSelection.
5. Crossover: Exchange of timetable segments between parent chromosomes.
6. Mutation: Random slot swaps to introduce diversity and avoid LocalOptima.
7. Termination: Stops after maximum generations or when acceptable fitness is achieved.

---

System Architecture

1. End Users: Administrator, Faculty, Students/Class Representatives.
2. Frontend Layer: Web UI and role-specific dashboards; timetable visualization (day-wise and slot-wise views); user actions such as generating, viewing, and requesting timetable changes.
3. Backend Layer: User management and authorization; constraint handling and validation; timetable creation, update, and retrieval services; coordination between frontend, optimization engine, and database.
4. Optimization Engine: Timetable generator, constraint evaluation engine, hard and soft constraint handler. Receives academic data and constraints from the backend, applies the Genetic Algorithm to generate optimized timetables, evaluates solutions using fitness functions, and returns conflict-free timetables to the backend.
5. Data Layer: Stores faculty details and availability, course information, classroom data and capacities, time slots, and generated timetables.
6. Testing Layer:
   • UI testing for frontend components
   • API and integration testing for backend services
   • Validation testing for timetable constraints and GA outputs
7. DevOps Layer:
   • Containerization using Docker
   • CI/CD pipelines for automated builds and testing
   • Deployment environment management
   • Monitoring and logging

---

Backend API

1. Course Assignments & Timetable Management

Method	Endpoint	Description
GET	/api/timetable	Get all course assignments
POST	/api/timetable	Create course assignment
GET	/api/timetable/find	Find assignments by parameters
PUT	/api/timetable/{id}	Update assignment
DELETE	/api/timetable/{id}	Delete assignment
PUT	/api/timetable/{id}/slots	Update timetable slots
PUT	/api/timetable/{id}/slot	Update single slot
POST	/api/timetable/sample/generate	Generate sample data

---

2. Generator & Validation APIs

Method	Endpoint	Description
POST	/api/generator/validate	Validate timetable
GET	/api/generator/constraints	Get constraints
POST	/api/generator/generate	Generate optimized timetable

---

3. Courses Management

Method	Endpoint	Description
GET	/api/courses/all	Get all courses
POST	/api/courses/add	Add a new course
PUT	/api/courses/update/{id}	Update course details
DELETE	/api/courses/{id}	Delete a course

---

4. Faculty Management

Method	Endpoint	Description
GET	/api/faculty/all	Get all faculty members
POST	/api/faculty/add	Add faculty member
PUT	/api/faculty/update/{id}	Update faculty details
DELETE	/api/faculty/{id}	Delete faculty member

---

5. Classroom Management

Method	Endpoint	Description
GET	/api/rooms/all	Get all classrooms
POST	/api/rooms/add	Add classroom
PUT	/api/rooms/update/{id}	Update classroom
DELETE	/api/rooms/{id}	Delete classroom

---

6. Timetable Constraints Management

Method	Endpoint	Description
GET	/api/constraints	Get all constraints
POST	/api/constraints	Create new constraint
GET	/api/constraints/default	Get default constraint template
GET	/api/constraints/active	Get active constraints
GET	/api/constraints/{id}	Get constraint by ID
PUT	/api/constraints/{id}	Update constraint
DELETE	/api/constraints/{id}	Delete constraint

---

7. Authentication

Method	Endpoint	Description
POST	/api/auth/login	Login using Microsoft OAuth
POST	/api/auth/logout	Logout user
POST	/api/auth/edit	Edit timetable (placeholder)

---

8. Dashboard

Method	Endpoint	Description
GET	/api/dashboard/stats	Get dashboard statistics

---

Testing

Objectives

• Ensure correctness of timetable generation and constraint enforcement.
• Validate backend API behavior and data integrity.
• Detect defects early through automated testing.
• Prevent regression during iterative development.
• Ensure acceptable performance during peak timetable generation.

Testing Levels and Tools

1. Unit Testing: Check individual functions. (Tool: Jest)
2. Integration Testing: Validate interactions between APIs, the database, and the optimization engine to ensure correct data flow. (Tools: Jest, Supertest)
3. API Testing: Validate backend APIs using Swagger to verify request/response formats, status codes, and error handling. (Tool: Swagger/OpenAPI)
4. Frontend Testing: Test UI components and user interactions using mocked backend responses. (Tools: Jest, Frontend Component Testing Framework)
5. End-to-End (E2E) Testing: Test complete user workflows such as login, timetable generation, and rescheduling. (Tools: Cypress or Playwright)
6. Performance Testing: Evaluate system performance under peak load conditions, particularly during timetable generation. (Tools: k6 or Artillery)

---

DevOps

Objectives

• Ensure consistent execution across development, testing, and deployment environments.
• Automate build, test, and deployment processes.
• Isolate compute-intensive timetable generation workloads.
• Maintain responsiveness for user-facing operations.
• Enable fast rollback and recovery from failures.
• Improve collaboration between development teams.

Continuous Integration (Tools: GitHub Actions)

Whenever code is pushed to the repository:

• Source code is automatically checked out.
• Docker images are built for all services.
• Basic validation and tests are executed.
• Build failures prevent deployment.

Continuous Deployment

• Container-based deployment using versioned Docker images.
• Successful CI builds generate deployable artifacts.
• Backend and optimization services are deployed independently.
• Automated health checks ensure service readiness.

Rollback

• Immediate rollback to the last stable Docker image on failure.
• Minimizes downtime during scheduling or constraint evaluation errors.

Optimization Workload Handling

Timetable generation involves validating hard constraints and optimizing soft constraints. To handle this efficiently:

• Optimization algorithms execute as background worker services.
• Backend APIs remain responsive during schedule generation.
• Optimization services can be scaled independently if workload increases.

Monitoring & Logging

Monitoring Includes:

• Timetable generation execution time
• API response latency
• Optimization service failures
• Resource utilization

Logging Captures:

• Timetable generation events
• Constraint validation failures
• Rescheduling requests and results

---

Conclusion

The Automated Timetable Management System provides an efficient and scalable solution for academic scheduling using a Genetic Algorithm–based optimization approach. It generates conflict-free timetables while satisfying key constraints such as faculty availability, room capacity, and workload balance.