Updated readme.

cair · Nov 12, 2024 · b4242e2 · b4242e2
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-# Job Shop Scheduling Algorithms
+# Performant Job Shop Scheduling (PER-JSP)
 
-This project implements various algorithms for solving the Job Shop Scheduling Problem (JSSP), including Q-Learning and Actor-Critic methods. It provides both a C++ library and Python bindings for easy integration into different environments.
+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
 
-- Job Shop Environment simulation
-- Q-Learning algorithm implementation
-- Actor-Critic algorithm implementation
-- Taillard problem instance generator
-- Visualization of scheduling results
-- Python bindings for easy integration
+- 🚀 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 | Implemented | Notes |
-|-----------|:-----------:|-------|
-| Q-Learning | ✅ | |
-| SARSA | ❌ | |
-| DQN (Deep Q-Network) | ❌ | |
-| Double DQN | ❌ | |
-| Dueling DQN | ❌ | |
-| Actor-Critic | ✅ | |
-| A2C (Advantage Actor-Critic) | ❌ | |
-| A3C (Asynchronous Advantage Actor-Critic) | ❌ | |
-| PPO (Proximal Policy Optimization) | ❌ | |
-| TRPO (Trust Region Policy Optimization) | ❌ | |
-| SAC (Soft Actor-Critic) | ❌ | |
-| DDPG (Deep Deterministic Policy Gradient) | ❌ | |
-| TD3 (Twin Delayed DDPG) | ❌ | |
-| MADDPG (Multi-Agent DDPG) | ❌ | |
-| QMIX (Q-Mixing) | ❌ | |
-| VDN (Value Decomposition Networks) | ❌ | |
-| Genetic Algorithms | ❌ | |
-| Particle Swarm Optimization | ❌ | |
-| Simulated Annealing | ❌ | |
-| Ant Colony Optimization | ❌ | |
-
-### Implemented Simulator Features
-| Feature | Implemented | Notes |
-|---------|:-----------:|-------|
-| Jobs | ✅ | |
-| Operations | ✅ | |
-| Actions | ✅ | |
-| Bindings | ✅ | |
-| Taillard | ✅ | |
-| Manual Environment Creator | ❌ | |
-| Tool Changing | ❌ | |
-| Tool Properties | ❌ | |
-| Machine Breakdowns | ❌ | |
-| Job Priority | ❌ | |
-| Setup Times | ❌ | |
-| Due Dates | ❌ | |
-
-## Dependencies
-
-This project uses vcpkg to manage C++ dependencies. The dependencies are specified in the `vcpkg.json` manifest file in the root directory of the project.
-
-Before building the project, ensure you have the following installed:
-
-### Build Tools
-```bash
-sudo apt-get install build-essential pkg-config cmake git curl zip unzip tar autoconf autoconf-archive libtool
-```
+| 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:
 
-### GLEW
 ```bash
-sudo apt-get install libxmu-dev libxi-dev libgl-dev libglu1-mesa-dev
+PYTHON_ONLY=1 pip install .
 ```
 
-### GLFW3
-```bash
-sudo apt-get install libxrandr-dev libxinerama-dev libxcursor-dev libxi-dev
-```
+This installation:
+- ✅ No C++ compiler needed
+- ✅ No system dependencies required
+- ✅ Quick installation
+- ❌ Lower performance compared to C++ version
 
-### Python3
-```bash
-sudo apt-get install python3 python3-dev
-```
+### 2. Full Installation (With C++ Extensions)
+For users who want maximum performance:
 
-### Performance Tools (for WSL2)
-```bash
-sudo apt-get install linux-tools-common linux-tools-generic
-sudo /usr/lib/linux-tools-6.8.0-36/perf
-```
-
-### vcpkg Setup
-
-To install vcpkg:
+First, install system dependencies:
 
 ```bash
-git clone https://github.com/Microsoft/vcpkg.git
-cd vcpkg
-./bootstrap-vcpkg.sh
+# 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
 ```
 
-Set the `VCPKG_ROOT` environment variable to the vcpkg installation directory:
-
+Then install the package:
 ```bash
-export VCPKG_ROOT=/path/to/vcpkg
+pip install .
 ```
 
-## Building the Project
+This installation:
+- ✅ Maximum performance
+- ✅ All features available
+- ❓ Requires system dependencies
+- ❓ Longer installation time
 
-This project uses CMakePresets.json to manage build configurations. There are two main configurations: debug and release.
+## Quick Start
 
-1. Clone the repository:
-   ```bash
-   git clone https://github.com/cair/job-shop-simulator.git
-   cd job-shop-simulator
-   ```
-
-2. To configure and build the project in debug mode:
-   ```bash
-   cmake --preset=debug
-   cmake --build --preset=debug
-   ```
+```python
+from per_jsp import Environment, QLearning
 
-3. To configure and build the project in release mode:
-   ```bash
-   cmake --preset=release
-   cmake --build --preset=release
-   ```
+# Create environment
+env = Environment.from_taillard(1)  # Load Taillard instance 1
 
-## Installing the Python Package
+# Create solver
+solver = QLearning(
+    env,
+    learning_rate=0.1,
+    discount_factor=0.9,
+    exploration_rate=0.1
+)
 
-To install the Python package, first ensure that the `VCPKG_ROOT` environment variable is set, then run:
+# Train
+solver.train(episodes=1000)
 
-```bash
-pip install .
+# Get solution
+schedule = solver.get_best_schedule()
+schedule.visualize()
 ```
 
-This will automatically use CMakePresets.json to build the project and install the Python package.
-
-## Usage
+## Advanced Usage
 
-### C++ Library
+### Custom Problem Instance
 
-To use the C++ library in your project, include the necessary headers and link against the library:
+```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)
+```
 
-```cpp
-#include <jobshop/job_shop_environment.h>
-#include <jobshop/job_shop_qlearning.h>
+### Using Different Solvers
 
-// Your code here
+```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
+)
 ```
 
-### Python Module
-
-After installing the Python package, you can use it in your Python code:
+## Performance Comparison
 
-```python
-import jobshop
+| 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   |
 
-# Create a job shop environment
-env = jobshop.JobShopEnvironment(jobs)
+## Contributing
 
-# Create a Q-Learning agent
-agent = jobshop.JobShopQLearning(env, alpha=0.1, gamma=0.9, epsilon=0.3)
+Contributions are welcome! See our [Contributing Guide](CONTRIBUTING.md) for details.
 
-# Train the agent
-agent.train(num_episodes=1000)
+### Development Setup
 
-# Print the best schedule
-agent.printBestSchedule()
-```
+```bash
+# Clone repository
+git clone https://github.com/cair/per-jsp
+cd per-jsp
 
-## Project Structure
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate  # or `venv\Scripts\activate` on Windows
 
-- `src/`: Contains the C++ source files
-- `include/`: Contains the header files
-- `bindings/`: Contains the Python bindings
-- `CMakeLists.txt`: The main CMake configuration file
-- `CMakePresets.json`: Defines the build presets
-- `vcpkg.json`: Specifies the project dependencies for vcpkg
+# Install in development mode
+pip install -e ".[dev]"
+```
 
 ## License
 
-This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+This project is licensed under the MIT License - see [LICENSE](LICENSE) for details.
 
-## Contributing
+## Citation
+
+If you use this software in your research, please cite:
 
-Contributions are welcome! Please feel free to submit a Pull Request.
+```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}
+}
+```
 
-## Contact
+## Support
 
-If you have any questions or feedback, please open an issue on the GitHub repository.
+- 📖 [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)