Simulation-based generation of robust heuristics for self-control of manual production processes: A hybrid approach on the way to industry 4.0.

在工业4.0的意义上，提出了完全自配置和自我控制的生产计划和控制(PPC)，以补偿干扰，同时满足较高的物流目标。实施这种购买力平价有许多概念，主要需要像在未来的网络物理系统(CP)中那样具有永久性的数据可用性。这种永久性数据可用性(实时数据和实时交互)目前仅在主要采用人工生产流程的行业中部分可行，并将在未来几年仍然是研究和开发的主题。一方面，人类工作的连续数字记录的技术实现和允许性尚不清楚。另一方面，机器的应用程序编程接口根本不能自由访问。即使在从现有生产系统向CPS的过渡和开发阶段，为了利用生产过程的自我控制在过程不确定的情况下改善物流目标变量，PPC功能的部分自动化变得必要，这使得混合PPC成为可能。作为一种概念方法，该项目追求基于中央和基于知识的稳健自我控制的配置，以基于现有信息或在定义的周期内容易在生产系统中收集的信息(类似于滚动波计划)进行资源分配和排序。这一方法旨在成为实施与购买力平价有关的工业4.0愿景的第一步，特别是对于具有高比例体力劳动和高度工艺不稳定性的行业。为了将计划中的研究项目与自我控制领域的现有研究区分开来，它的基础是数据质量和可用性明显降低，这一工业部门未来的情况仍将如此。在这种情况下，必须开发全新的方法和程序来实施概念方法。由于实时数据和数据交换的可能性有限，因此该项目的重点是开发用于自我控制的稳健启发式方法，以便在不进行永久重新配置的情况下做出可能的最佳顺序和资源分配决策。因此，在产生这些试探法时，必须预期地将扰动包括在内。此外，通过机器学习从模拟和生产中获得的隐性知识将被用于持续改进自我控制。

In the sense of Industry 4.0, complete self-configuration and self-control of production planning and control (PPC) is proposed to compensate for disturbances while at the same time meeting high logistical targets. Numerous concepts exist for the implementation of such a PPC, which predominantly require permanent data availability as in future cyber-physical systems (CPS). This permanent data availability (real-time data and real-time interaction) is currently only partially feasible in industries with mainly manual production processes and will remain the subject of research and development for the coming years. On the one hand, the technological implementation and permissibility of a continuous digital recording of human work is unclear. On the other hand, interfaces for application programming of machines are simply not freely accessible. In order to take advantage of the self-control of production processes with regard to the improvement of logistical target variables under process uncertainties even in the transition and development phase from existing production systems to CPS, the partial automation of PPC functions becomes necessary, which makes it possible to speak of a hybrid PPC.As a concept approach, the project pursues the central and knowledge-based configuration of robust self-control for sequencing and resource allocation based on existing information or information that is easy to collect in the production system for a defined period (similar to rolling-wave planning). This approach is intended to be a first step in the implementation of the Industry 4.0 vision in relation to PPC, especially for industries with the characteristics of a high proportion of manual work and high process instability. In order to distinguish the planned research project from existing research in the field of self-control, it is based on significantly lower data quality and availability, as will still be the same conditions in this industrial sector in the future. Under these conditions, completely new methods and procedures for implementing the conceptual approach must be developed.Due to the limited real-time data and data exchange possibilities, the focus of the project is thus on the development of robust heuristics for self-control in order to make the best possible sequence and resource allocation decisions without permanent reconfiguration. Disturbances must therefore be included prospectively in the generation of these heuristics. Furthermore, implicit knowledge from simulation and production through machine learning is to be used for the continuous improvement of self-control.