Task Scheduler Optimization System is a Data Structures and Algorithms based project that optimizes task execution using scheduling techniques. The system allows users to create tasks with priorities, deadlines, execution times, profit scores, required skills, and dependencies. Resources can also be added with specific skills and working-hour constraints.
The scheduler processes all tasks and resources, applies scheduling logic using Sorting, Priority Queues, Heaps, and Greedy Algorithms, and generates an optimized execution schedule. The system provides analytics, resource utilization reports, schedule timelines, and performance metrics through a modern dashboard.
This project demonstrates how scheduling logic used in operating systems, project management tools, cloud computing platforms, and enterprise planning systems can be implemented using core DSA concepts.
Managing multiple tasks manually becomes difficult when deadlines, priorities, dependencies, execution times, and resource constraints are involved.
The main challenges include:
- Deciding which task should execute first
- Allocating resources efficiently
- Minimizing missed deadlines
- Maximizing overall profit and productivity
- Managing task dependencies correctly
- Monitoring resource utilization
This project solves these challenges by automatically generating an optimized task schedule.
- Dynamic task creation
- Dynamic resource management
- Priority-based scheduling
- Deadline-aware scheduling
- Dependency handling
- Skill-based resource assignment
- Optimized task execution order
- Completed and missed task tracking
- Total profit calculation
- Resource utilization monitoring
- Interactive schedule timeline
- Performance analytics dashboard
- Modern responsive user interface
- FastAPI backend
- React frontend
- CSV dataset support
- GitHub-ready project structure
Tasks are sorted according to deadlines, priorities, and profit scores.
Tasks are selected efficiently using a priority queue.
Heap-based scheduling improves task selection performance.
The scheduler selects the best possible task at each step to maximize efficiency.
Used for task processing and scheduling flow.
Tasks with dependencies are executed only after prerequisite tasks are completed.
Task ordering is optimized based on multiple constraints.
- Sorting: O(n log n)
- Heap Operations: O(log n)
- Scheduling: O(n log n)
Task Input
↓
Task Validation
↓
Sorting
↓
Priority Queue Processing
↓
Scheduling Algorithm
↓
Resource Assignment
↓
Dependency Verification
↓
Optimized Schedule Generation
↓
Timeline Visualization
↓
Performance Analytics
↓
Final Report Generation
Each task contains:
- Task ID
- Task Name
- Priority
- Deadline
- Execution Time
- Profit Score
- Required Skill
- Dependencies
Each resource contains:
- Resource ID
- Resource Name
- Skills
- Shift Start Time
- Shift End Time
- Maximum Working Hours
The system validates:
- Empty fields
- Invalid deadlines
- Invalid execution times
- Missing skills
- Invalid dependencies
Tasks are sorted based on:
- Deadline
- Priority
- Profit Score
Tasks are inserted into a heap-based priority queue for efficient selection.
The greedy scheduler:
- Selects the highest-priority feasible task
- Checks resource availability
- Checks required skills
- Verifies dependencies
- Assigns start and end times
The system calculates:
- Total tasks
- Completed tasks
- Missed tasks
- Total profit
- Completion rate
- Missed task rate
- Resource utilization
Task-Scheduler-Optimization-System/
│
├── backend/
│ ├── requirements.txt
│ ├── run.py
│ └── app/
│ ├── main.py
│ ├── models.py
│ └── scheduler.py
│
├── frontend/
│ ├── index.html
│ ├── package.json
│ └── src/
│ ├── main.jsx
│ └── style.css
│
├── data/
│ ├── sample_tasks.csv
│ └── sample_resources.csv
│
├── docs/
│ └── architecture.md
│
├── images/
│ ├── dashboard-home.png
│ ├── task-management.png
│ ├── resource-management.png
│ ├── schedule-timeline.png
│ └── reports-analytics.png
│
├── outputs/
│
├── README.md
├── .gitignore
└── main.py
python -m venv venv.\venv\Scripts\Activate.ps1pip install -r backend\requirements.txtpython backend\run.pyBackend runs on:
http://127.0.0.1:8000
Open a new terminal:
cd frontend
npm install
npm run devOpen:
http://localhost:5173
T1 | Design UI | Priority 5 | Deadline 48 | Duration 3 | Profit 90 | frontend
T2 | Build API | Priority 4 | Deadline 60 | Duration 4 | Profit 120 | backend | Depends on T1
T3 | Test Flow | Priority 4 | Deadline 72 | Duration 2 | Profit 80 | qa | Depends on T2
R1 | Backend Engineer | backend|qa | Shift 0-24 | Max Hours 24
R2 | Frontend Developer | frontend | Shift 0-24 | Max Hours 24
Completed Tasks : 3
Missed Tasks : 0
Total Profit : 290
Optimized Schedule:
T1 Design UI Start: 0 End: 3
T2 Build API Start: 3 End: 7
T3 Test Flow Start: 7 End: 9
Completion Rate : 100%
Displays:
- Total Tasks
- Completed Tasks
- Missed Tasks
- Total Profit
- Add tasks
- Edit tasks
- Delete tasks
- Add resources
- Manage skills
- Configure shifts
- Optimized schedule
- Gantt-style timeline
- Resource assignments
- Completion rate
- Missed task rate
- Profit metrics
- Resource utilization
- Project Management Systems
- Operating System Scheduling
- Cloud Job Scheduling
- Workforce Planning
- Manufacturing Scheduling
- Resource Allocation Systems
- Enterprise Planning Tools
Through this project, I learned:
- Sorting techniques
- Heap and Priority Queue implementation
- Greedy scheduling algorithms
- Dependency handling
- Resource allocation strategies
- Task optimization techniques
- FastAPI backend development
- React frontend development
- Dashboard design
- GitHub project documentation
- Software engineering practices
- Practical DSA implementation
- CP-SAT Optimization
- Integer Linear Programming
- Multi-resource scheduling
- Real-time notifications
- Authentication system
- Database integration
- Team collaboration features
- Exportable reports
- Advanced analytics
- AI-assisted scheduling recommendations
Vaidehi Deore