Job Shop Scheduling Algorithms

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.

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

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

sudo apt-get install build-essential pkg-config cmake git curl zip unzip tar autoconf autoconf-archive libtool

GLEW

sudo apt-get install libxmu-dev libxi-dev libgl-dev libglu1-mesa-dev

GLFW3

sudo apt-get install libxrandr-dev libxinerama-dev libxcursor-dev libxi-dev

Python3

sudo apt-get install python3 python3-dev

Performance Tools (for WSL2)

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:

git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh

Set the VCPKG_ROOT environment variable to the vcpkg installation directory:

export VCPKG_ROOT=/path/to/vcpkg

Building the Project

This project uses CMakePresets.json to manage build configurations. There are two main configurations: debug and release.

1. Clone the repository:

   git clone https://github.com/cair/job-shop-simulator.git
   cd job-shop-simulator
   

2. To configure and build the project in debug mode:

   cmake --preset=debug
   cmake --build --preset=debug
   

3. To configure and build the project in release mode:

   cmake --preset=release
   cmake --build --preset=release
   

Installing the Python Package

To install the Python package, first ensure that the VCPKG_ROOT environment variable is set, then run:

pip install .

This will automatically use CMakePresets.json to build the project and install the Python package.

Usage

C++ Library

To use the C++ library in your project, include the necessary headers and link against the library:

#include <jobshop/job_shop_environment.h>
#include <jobshop/job_shop_qlearning.h>

// Your code here

Python Module

After installing the Python package, you can use it in your Python code:

import jobshop

# Create a job shop environment
env = jobshop.JobShopEnvironment(jobs)

# Create a Q-Learning agent
agent = jobshop.JobShopQLearning(env, alpha=0.1, gamma=0.9, epsilon=0.3)

# Train the agent
agent.train(num_episodes=1000)

# Print the best schedule
agent.printBestSchedule()

Project Structure

• 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

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Contact

If you have any questions or feedback, please open an issue on the GitHub repository.