Learning to Solve Job Shop Scheduling under Uncertainty
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909156772937728 |
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| author | Infantes, Guillaume Roussel, Stéphanie Pereira, Pierre Jacquet, Antoine Benazera, Emmanuel |
| author_facet | Infantes, Guillaume Roussel, Stéphanie Pereira, Pierre Jacquet, Antoine Benazera, Emmanuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Solve Job Shop Scheduling under Uncertainty Infantes, Guillaume Roussel, Stéphanie Pereira, Pierre Jacquet, Antoine Benazera, Emmanuel Artificial Intelligence Machine Learning 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. |
| title | Learning to Solve Job Shop Scheduling under Uncertainty |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.01308 |