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

基于模拟的稳健启发式生成，用于手动生产流程的自我控制：迈向工业 4.0 的混合方法。

• 批准号: 418727532
• 负责人: Professor Dr. Uwe Aßmann
• 金额: --
• 依托单位: Professur für Technische Logistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/418727532?language=en
• 关键词: Simulation; based; generation; robust; heuristics

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.

在工业4.0的意义上，生产计划和控制（PPC）的完全自我配置和自我控制被提出来补偿干扰，同时满足高物流目标。存在许多概念来实现这样的PPC，其主要要求永久的数据可用性，如在未来的网络物理系统（CPS）中。这种永久数据可用性（实时数据和实时交互）目前在主要采用手工生产流程的行业中仅部分可行，并将继续成为未来几年的研究和开发主题。一方面，对人类工作进行连续数字记录的技术实施和允许性尚不清楚。另一方面，用于机器的应用程序编程的接口根本不能自由访问。为了利用生产过程的自我控制，即使在从现有生产系统到CPS的过渡和开发阶段，也可以在过程不确定性下改善物流目标变量，PPC功能的部分自动化变得必要，这使得可以谈论混合PPC。作为概念方法，该项目力求根据现有信息或生产系统在规定时期内易于收集的信息（类似于滚波规划），对排序和资源分配进行中央和基于知识的强大自我控制配置。这种方法旨在成为与PPC相关的工业4.0愿景的第一步，特别是对于具有高比例手工工作和高过程不稳定性的行业。为了将计划中的研究项目与自控领域的现有研究区分开来，它是基于明显较低的数据质量和可用性，因为未来这一工业部门的条件仍然相同。在这些条件下，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的化学方法，以便在没有永久重新配置的情况下做出最佳的序列和资源分配决策。因此，在产生这些解释学时，必须前瞻性地包括扰动。此外，通过机器学习从模拟和生产中获得的隐含知识将用于持续改进自我控制。