Predictive-reactive scheduling for increasing the robustness of agile production systems

• Applicant: Professorin Dr.-Ing. Nicole Stricker
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 455094120

The manufacturing industry is faced with an increasingly uncertain and fast-paced environment. Product lifecycles are shortening while the variant diversity steadily increases. Additionally, customer demand is harder to predict and more volatile than in the past. In order to copy with these challenges, agile production systems are a subject of current research. The term comprises production systems that are able to dynamically adapt to internal and external influencing factors (e.g. shift of the variant mix, change of production volume, integration of new products). Agile production systems are decoupled from the takt due to their loose interlinkage and allow a production in lot size 1 by the use of automated guided vehicles (AGVs).Decisive for the efficiency of agile production systems is a production control system that meets the various requirements: It must be able to take dynamic influences such as machine failures into account right from the planning stage. This requires a predictive planning process that takes the availability of machines into account. In addition, in the event of unexpected disruptions, it must offer rescheduling that exploits the degrees of freedom of agile production systems. This requires a reactive planning process with which a high solution quality can be achieved in a very short computing time. Finally, agile production systems should be usable in just-in-sequence production, i.e. orders should be handed over at a defined point in time and at a defined location, e.g. to downstream production stages. This requires a combination of predictive and reactive planning procedures to achieve the desired adherence to schedules, even under dynamic influences. The production control should therefore allow the highest possible robustness.The present project aims at the research of such a method, which allows a high robustness to enable a JIS production under dynamic influences. A special focus is on the answering of the question how robust the predictive planning has to be in order to enable the reactive rescheduling to realize the desired adherence to schedules.

• DFG Programme: Research Grants
• Co-Investigator: Professorin Dr.-Ing. Gisela Lanza