As industrial manufacturing and automation systems grow in complexity, there is a need for control software engineering support. Soft-Commissioning and Reality in the Loop (RIL) are two novel approaches which allow coupling simulation models to real world entities and allow the analyst to pre-commission and test the behavior of a system, before it is completely built in reality. To be flexible and fast in building up a simulation model fulfilling the requirements of Soft-Commissioning and RIL there is a need for a component-based modeling architecture. In this paper we define the characteristic requirements, and derive an architecture out of them, which is discussed from different aspects. Finally we briefly present a simple example.
Production Fine Planning is often performed directly using all information and assuming that it is fixed. In practice, however, this information changes regularly and the plan has to be adapted. This often means a complete rescheduling of all operations. We present a new approach to this problem by optimizing priority rules that can sort the available next actions. These priority rules often yield similar results even though they do not resemble each other. By using genetic programming to build these priority rules, a distributed system to compute the simulations and a solution archive with a cache of hundreds of thousands of priority rules, new insights into priority rule-based optimization are gained. This archive does not only speed up calculation by avoiding re-simulation of the same rule but can provide a pseudo Pareto front of shorter sub-optimal solutions that facilitate interpretation of the more complex rules and their evolution during the optimization process.
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