A Recommendation System-Based Framework for Enhancing Human-Machine Collaboration in Industrial Timetabling Rescheduling: Application in Preventive Maintenance
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911365506007040 |
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| author | Ducharlet, Kévin Zhang, Liwen Maqrot, Sara Saidi, Houssem |
| author_facet | Ducharlet, Kévin Zhang, Liwen Maqrot, Sara Saidi, Houssem |
| contents | Industrial timetabling is a critical task for decision-makers across various sectors to ensure efficient system operation. In real-world settings, it remains challenging because unexpected events often disrupt execution. When such events arise, effective rescheduling and collaboration between humans and machines becomes essential. This paper presents a recommendation system-based framework for handling rescheduling challenges, built on Timefold, a powerful AI-driven planning engine. Our experimental study evaluates nine instances inspired by a realworld preventive maintenance use case, aiming to identify the heuristic that best balances solution quality and computing time to support near-optimal decisionmaking when rescheduling is required due to unexpected events during operational days. Finally, we illustrate the complete process of our recommendation system through a simple use case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Recommendation System-Based Framework for Enhancing Human-Machine Collaboration in Industrial Timetabling Rescheduling: Application in Preventive Maintenance Ducharlet, Kévin Zhang, Liwen Maqrot, Sara Saidi, Houssem Human-Computer Interaction Artificial Intelligence Industrial timetabling is a critical task for decision-makers across various sectors to ensure efficient system operation. In real-world settings, it remains challenging because unexpected events often disrupt execution. When such events arise, effective rescheduling and collaboration between humans and machines becomes essential. This paper presents a recommendation system-based framework for handling rescheduling challenges, built on Timefold, a powerful AI-driven planning engine. Our experimental study evaluates nine instances inspired by a realworld preventive maintenance use case, aiming to identify the heuristic that best balances solution quality and computing time to support near-optimal decisionmaking when rescheduling is required due to unexpected events during operational days. Finally, we illustrate the complete process of our recommendation system through a simple use case. |
| title | A Recommendation System-Based Framework for Enhancing Human-Machine Collaboration in Industrial Timetabling Rescheduling: Application in Preventive Maintenance |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2601.06029 |