Learning to Solve Job Shop Scheduling under Uncertainty
<|reference_start|>Learning to Solve Job Shop Scheduling under Uncertainty: 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.<|reference_end|>
@article{infantes2024learning,
    title={Learning to Solve Job Shop Scheduling under Uncertainty},
    author={Guillaume Infantes and St'ephanie Roussel and Pierre Pereira and
  Antoine Jacquet and Emmanuel Benazera},
    journal={arXiv preprint arXiv:2404.01308},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01308},
    primaryClass={cs.AI cs.LG stat.ML}
}