https://github.com/cair/per-jsp
Performant execution runtime for JSP
https://github.com/cair/per-jsp
Last synced: 13 days ago
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Performant execution runtime for JSP
- Host: GitHub
- URL: https://github.com/cair/per-jsp
- Owner: cair
- License: mit
- Created: 2024-07-18T17:57:17.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-11-15T13:43:11.000Z (5 months ago)
- Last Synced: 2024-11-15T14:33:46.403Z (5 months ago)
- Language: Python
- Homepage:
- Size: 355 KB
- Stars: 0
- Watchers: 3
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Performant Job Shop Scheduling (PER-JSP)
A high-performance Job Shop Scheduling Problem (JSSP) solver with C++ core and Python bindings. The project provides both a fast C++ library and intuitive Python interface for solving JSSP using various algorithms including Q-Learning and Actor-Critic methods.
## Features
- 🚀 High-performance C++ core with Python bindings
- 🐍 Pure Python fallback implementation
- 🔧 Flexible environment configuration
- 📊 Built-in visualization
- 📈 Support for standard benchmark problems (Taillard)
- 🧮 Multiple solver algorithms
### Implemented Algorithms
| Algorithm | Status | Implementation |
|-----------|:------:|----------------|
| Q-Learning | ✅ | C++/Python |
| Actor-Critic | ✅ | C++/Python |
| SARSA | ❌ | Planned |
| DQN | ❌ | Planned |
| PPO | ❌ | Planned |
| DDPG | ❌ | Planned |
### Environment Features
| Feature | Status | Notes |
|---------|:------:|-------|
| Jobs/Operations | ✅ | Full support |
| Taillard Benchmarks | ✅ | Built-in |
| Custom Environments | ✅ | JSON format |
| Machine Breakdowns | 🚧 | In progress |
| Tool Management | 🚧 | In progress |
| Priority Scheduling | 🚧 | Planned |
## Installation
There are two ways to install PER-JSP:
### 1. Python-Only Installation (Fast Install)
For users who only need the Python implementation without C++ optimizations:
```bash
PYTHON_ONLY=1 pip install .
```
This installation:
- ✅ No C++ compiler needed
- ✅ No system dependencies required
- ✅ Quick installation
- ❌ Lower performance compared to C++ version
### 2. Full Installation (With C++ Extensions)
For users who want maximum performance:
First, install system dependencies:
```bash
# Ubuntu/Debian
sudo apt-get update && sudo apt-get install -y \
build-essential \
cmake \
ninja-build \
git \
pkg-config \
libgl-dev \
libglu1-mesa-dev \
libxrandr-dev \
libxinerama-dev \
libxcursor-dev \
libxi-dev \
python3-dev
# macOS
brew install cmake ninja pkg-config
# Windows (with Visual Studio installed)
# No additional dependencies needed
```
Then install the package:
```bash
pip install .
```
This installation:
- ✅ Maximum performance
- ✅ All features available
- ❓ Requires system dependencies
- ❓ Longer installation time
## Quick Start
```python
from per_jsp import Environment, QLearning
# Create environment
env = Environment.from_taillard(1) # Load Taillard instance 1
# Create solver
solver = QLearning(
env,
learning_rate=0.1,
discount_factor=0.9,
exploration_rate=0.1
)
# Train
solver.train(episodes=1000)
# Get solution
schedule = solver.get_best_schedule()
schedule.visualize()
```
## Advanced Usage
### Custom Problem Instance
```python
from per_jsp import Environment
# Define your problem
problem = {
"jobs": [
{"operations": [
{"machine": 0, "processing_time": 10},
{"machine": 1, "processing_time": 20}
]},
{"operations": [
{"machine": 1, "processing_time": 15},
{"machine": 0, "processing_time": 25}
]}
]
}
# Create environment
env = Environment.from_dict(problem)
```
### Using Different Solvers
```python
from per_jsp import Environment, ActorCritic
env = Environment.from_taillard(1)
# Actor-Critic solver
solver = ActorCritic(
env,
actor_lr=0.001,
critic_lr=0.001,
discount_factor=0.99
)
# Train with specific settings
solver.train(
episodes=1000,
max_steps=10000,
verbose=True
)
```
## Performance Comparison
| Problem Size | Python-Only | With C++ | Speedup |
|-------------|-------------|----------|---------|
| 6x6 | 1.00x | 8.45x | 8.45x |
| 10x10 | 1.00x | 12.3x | 12.3x |
| 20x20 | 1.00x | 15.7x | 15.7x |
## Contributing
Contributions are welcome! See our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
# Clone repository
git clone https://github.com/cair/per-jsp
cd per-jsp
# Create virtual environment
python -m venv venv
source venv/bin/activate # or `venv\Scripts\activate` on Windows
# Install in development mode
pip install -e ".[dev]"
```
## License
This project is licensed under the MIT License - see [LICENSE](LICENSE) for details.
## Citation
If you use this software in your research, please cite:
```bibtex
@software{andersen2024perjsp,
author = {Andersen, Per-Arne},
title = {PER-JSP: A Performant Job Shop Scheduling Framework},
year = {2024},
url = {https://github.com/cair/per-jsp}
}
```
## Support
- 📖 [Documentation](https://github.com/cair/per-jsp/wiki)
- 🐛 [Issue Tracker](https://github.com/cair/per-jsp/issues)
- 💬 [Discussions](https://github.com/cair/per-jsp/discussions)