Self-optimizing decentralized production control

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Facing a volatile market, an increasing variety of products and consequently increasingly complex manufacturing systems, production control is of particular importance in manufacturing. Due to the need for more frequent adaptation of process chains and production resources, centralized production control approaches are reaching their limits. In contrast, decentralized control approaches are characterized by high flexibility and quick adaptability. With the development of cyber-physical production systems (CPPS), technologies are now available for the first time with which comprehensive order-related data acquisition, communication between production components and the use of local computing capacities can be realized. This is an essential prerequisite for the introduction of decentralized control approaches. From a methodological point of view, however, local optimization tendencies of the algorithms represent a further obstacle. Experience-based consideration of the global optimum in local decision-making can eliminate this disadvantage.The main objective of the proposed project is the fundamental research of a method for decentralized production control that allows autonomous and variable decision-making with simultaneous experience-based self-optimization with regard to the global system performance of the production system. The method should take special account of the requirements of shop-floor production. In order to achieve the project goals, a suitable system architecture for decentralized production control is first examined within the framework of a CPPS. Subsequently, a method is conceived that allows decentralized and variable decision making with experience-based consideration of global system performance. The valuation basis required for this is formed by a system of key figures to be examined. A test environment is set up for the subsequent research. For this purpose, a simulation model of representative shop-floor production is first created. The software-implemented control method that controls the material flow within the simulation model is linked to this. Finally, simulation experiments are carried out within the framework of a structured test plan, which provide in-depth and scientifically substantial knowledge of the potentials and limits of the autonomous and learning control method being researched.