用推荐系统提升工厂排班人机协作效率，应对突发干扰
A Recommendation System-Based Framework for Enhancing Human-Machine Collaboration in Industrial Timetabling Rescheduling: Application in Preventive Maintenance
- 基于Timefold构建人机协同排程推荐框架
- 在9个真实维护场景中验证，平衡解质量与计算时间
- 适合制造业排程优化与智能决策系统研究者
工业排班是各行业保障系统高效运行的关键决策任务。现实中，意外事件常导致执行中断，此时人机协同的高效重排程尤为关键。本文提出一种基于推荐系统的重排程框架，依托Timefold这一强大的人工智能驱动规划引擎。实验基于一个真实的预防性维护应用场景，评估了九个实例，旨在识别在解质量与计算时间之间取得最佳平衡的启发式算法，以支持运营日突发情况下的近似最优决策。最后，通过一个简单案例展示了推荐系统的完整流程。
原文摘要 · Abstract (English)
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.
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