Operational and online planning of maintenance and production

生产和维护的核心冲突是维护措施必须确保机器的高可用性。然而，为了实现它们，通常需要在短时间内停机。特别是在复杂、多阶段和高利用率的生产系统中，生产和维护计划的冲突行为特别明显，因为机器停机会迅速导致整个生产系统的生产损失和故障后果。现有的科学方法并不能解决这些问题，因为真实的生产系统的复杂性是不充分的，并且是在非常严格的假设下（例如：G.在DFG资助的科学项目MK-ProInst的框架内，在生产工程和机床研究所（IFW）通过离散事件仿真（DES）开发并实施了一种协调生产和维护计划的动态规划方法。这种方法允许在生产过程中实施维护措施时产生的故障后果的鲁棒预测和量化。此外，规划备选方案可以得到和评估（e。G.替代维护开始时间）。该方法的验证表明，生产和维护成本可以降低高达9%。此外，规划质量显着提高，因为真实的生产系统的复杂性，动态性和随机性考虑使用仿真技术的决策。该方法的工业开发的基础是减少模型创建和适应所需的时间，并在早期阶段显示低模型有效性。知识转移项目的目标是开发动态规划方法，为各种公司提供有效的在线应用程序奠定必要的基础。为此，现有的规划方法将在基于BDE-/MDE-系统数据的面向流程的模拟模型的学习和自参数化方法方面得到扩展（例如：G.对于随机数据）。此外，将开发适当的反馈回路，以及时识别低模型有效性。这些方面是时间效率模型创建和调整以及实际操作（在线）应用的基础。

The central conflict of production and maintenance is the fact that maintenance measures have to ensure a high availability of machines. However, for their implementation a machine downtime is often required at short notice. Especially in complex, multi-stage and high utilized production systems, the conflicting behavior of production and maintenance planning is particularly pronounced, since the machine downtime can quickly cause production losses and failure consequences for the entire production system. Existing scientific approaches do not solve these issues, since the complexity of real production systems is mapped inadequate and under very restrictive assumptions (e. g. single machine approaches, deterministic and static decision environment).In the framework of the DFG-funded scientific project MK-ProInst, a dynamic planning approach to coordinate the production and maintenance planning was developed and implemented by means of discrete event simulation (DES) at the Institute of Production Engineering and Machine Tools (IFW). This approach allows a robust prediction and quantification of the resulting failure consequences that arise when implementing a maintenance measure during production. Moreover, planning alternatives can be derived and evaluated (e. g. alternative maintenance starting time). A validation of the approach showed that the production and maintenance costs can be reduced by up to 9 %. Furthermore, the planning quality is significantly increased since the complexity, dynamics and stochastics of real production systems are considered using the simulation technology in decision making. The basis for industrial exploitation of the methodology is to reduce the time needed for model creation and adaption and to show a low model validity at an early stage.The goal of the knowledge transfer project is the exploitation of the dynamic planning approach to achieve the essential foundation for an efficient and online application for a variety of companies. For this, the existing planning approach will be expanded with regard to methods for learning and self-parameterization of process-oriented simulation models based on data from BDE-/MDE-Systems (e. g. for stochastic data). In addition, appropriate feedback loops will be developed to the timely recognition of a low model validity. These aspects are the basis for time-efficient model creation and adaption and the operational (online) application in practice.