Traditional methods of scheduling are mostly based on the use of pieces of information directly related to the performance of schedules, as for instance processing times, delivery dates, etc., assuming that the production system is operating normally. In the case of malfunctions, the literature concentrates on the ensuing corrective operations, like scheduling with machine breakdowns or under remanufacturing considerations. These event-driven approaches are mainly used in dynamic scheduling or rescheduling systems. Unlike those, Smart Manufacturing and Industry 4.0 production environments integrate the physical and decision-making aspects of manufacturing processes in order to achieve their decentralization and autonomy. On these grounds we propose a data-driven architecture for scheduling, in which the system has real time access to data. Then, scheduling decisions can be made ahead of time, on the basis of more information. This promising approach is based on the architecture of cyber-physical systems, with a data-driven engine that uses, in particular, Big Data techniques to extract vital information for Industry 4.0 systems.

传统的调度方法主要基于使用与调度性能直接相关的信息，例如处理时间，交货日期等，假设生产系统运行正常。在出现故障的情况下，文献集中在随后的纠正操作上，例如因机器故障或出于再制造考虑而进行的计划。这些事件驱动的方法主要用于动态调度或重新调度系统中。与那些制造商不同，智能制造和工业4.0生产环境集成了制造过程的物理和决策方面，以实现其分散和自治。基于这些理由，我们提出了一种数据驱动的调度体系结构，其中系统可以实时访问数据。然后，可以根据更多信息提前做出调度决策。这种有前途的方法基于网络物理系统的体系结构，并具有一个数据驱动引擎，该引擎特别使用大数据技术为工业4.0系统提取重要信息。