JSSPetri: Job Shop Scheduling with Petri Nets

JSSPetri is a Python-based framework designed for simulating and analyzing job shop scheduling problems using Petri nets. The project provides an OpenAI Gym-compatible environment, allowing users to model and explore scheduling strategies in a simulated manufacturing setting.

Features

• Petri Net Modeling: JSSPetri utilizes Petri nets to model the flow of operations through different stages of production in a job shop scheduling environment. This provides a visual representation of the system dynamics.

• OpenAI Gym Compatibility: The environment is compatible with the OpenAI Gym framework, enabling seamless integration with reinforcement learning algorithms and facilitating experimentation with various scheduling strategies.

• Taillard Benchmarks Integration: JSSPetri supports the loading of Taillard Benchmarks instances, allowing users to benchmark and evaluate scheduling algorithms on standard datasets.

• Flexibility for Experimentation: The project is designed for flexibility, allowing researchers and practitioners to experiment with different scheduling policies. This flexibility enables the evaluation of the impact of various strategies on critical metrics such as makespan and resource utilization.

Framework Overview

Installation

You can install JSSPetri using pip:

pip install jsspetri