A simulation-based and flexi-time applying prediction model for scheduling personnel deployment times in the production planning process of cyber-physical systems

在工业4.0的背景下，生产计划和控制的预期权力下放，对人员规划提出了新的挑战。即使人仍然是智能工厂的关键因素，人工活动也将发生变化。部署时间将发生变化，工作内容将需要更具体的资格，导致部署连续性降低。同时，由于机器和订单的自主性与实时控制相结合，很难提前推断出具体的需求时间，而只是在实际需求中进行识别。目前还没有方法来应对这些变化的情况。为了确保有效的资源规划，尽管产生了时间冲突，该项目正在为分散的生产系统中的作业人员需求开发一个基于模拟的预测模型。通过纳入灵活的工作时间，人员能力能够适应需求，从而使分散控制系统能够考虑到秩序、机器和所有人员的总体目标。对于后者，不仅追求公司目标，而且追求员工个人的目标，基于模拟的方法考虑了现实中发生的随机不确定性，如波动的过程时间或故障。预测模型着眼于短期规划范围。在第一步中，使用控制参数(例如，人员部署时间)来生成场景，并且根据聚焦于目标功能的优化器来改变场景。将解决方案质量较高的场景组合成一个随机总体计划，并从该计划中得出确定性的部署时间。此外，对该方法的可行性、先决条件、潜力和限制(例如，工作时间模型的影响)进行了审查。

The expected decentralization of production planning and control within the context of Industry 4.0 poses new challenges for personnel planning. Even if the human being remains a key factor in the smart factory, manual activities will change. Deployment times will shift and work content will require more specific qualifications, resulting in a reduced deployment continuity. At the same time, due to the autonomy of machines and orders in combination with real-time control, it is hardly possible to deduce the concrete times of requirement in advance; instead, it will be just identified during the actual requirement. There are currently no methods available for coping with these changed conditions. In order to ensure efficient resource planning despite the resulting time conflict, the project is developing a simulation-based forecasting model for operative personnel requirements in decentralized production systems. By including flexible working times, the personnel capacity is adapted to the demand in such a way that the decentralized control system can take into account the overall objectives of order, machine and above all personnel. For the latter, not only company goals but also goals of individual employees are pursued.The simulation-based approach takes into account stochastic uncertainties that occur in reality, such as fluctuating process times or failures. The forecast model focuses on a short-term planning horizon. In a first step, scenarios are generated using control parameters (e.g. personnel deployment times) and varied according to an optimizer focusing on the target function. Scenarios of high solution quality are combined to a stochastic overall plan, from which deterministic deployment times are derived. Furthermore, the methodology is examined for feasibility, prerequisites, potentials and limits (e.g. influence of working time models).