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

• Applicant: Professor Dr.-Ing. Thorsten Schmidt
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 439188616

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).

• DFG Programme: Research Grants