Job-Shop Scheduling Problem (JSSP) is a combinatorial optimization problem where tasks need to be scheduled on machines in order to minimize criteria such as makespan or delay. To address more realistic scenarios, we associate a probability distribution with the duration of each task. Our objective is to generate a robust schedule, i.e. that minimizes the average makespan. This paper introduces a new approach that leverages Deep Reinforcement Learning (DRL) techniques to search for robust solutions, emphasizing JSSPs with uncertain durations. Key contributions of this research include: (1) advancements in DRL applications to JSSPs, enhancing generalization and scalability, (2) a novel method for addressing JSSPs with uncertain durations. The Wheatley approach, which integrates Graph Neural Networks (GNNs) and DRL, is made publicly available for further research and applications.
@article{arxiv.2404.01308,
title = {Learning to Solve Job Shop Scheduling under Uncertainty},
author = {Guillaume Infantes and Stéphanie Roussel and Pierre Pereira and Antoine Jacquet and Emmanuel Benazera},
journal= {arXiv preprint arXiv:2404.01308},
year = {2024}
}