
Reports and Papers
Simulation and OptimizationAnother example of the integration of different methods is the coupling of simulation and optimization: optimization usually requires an (often gross) simplification of the problem representation to become tractable. Simulation models, on the other hand, while capable of representing almost arbitrary levels of detail and complexity, are rarely capable of solving inverse problems, ie., determining the necessary set of inputs or controls to reach a desired outcome. One can, however, combine the approaches in that a simplified model (eg., steady state and spatially aggregated) is used as the basis for optimization; the result of the optimization is then used as the basis for a more detailed, eg., dynamic and spatially distributed simulation model, that also keeps track of the criteria, objectives and constraints used for the optimization, but with a higher degree of spatial and temporal resolution, and possibly a more refined process description. If, in the simulation run, constraints are violated or objectives not met, the corresponding values can be tightened or relaxed in the optimization to obtain a new solution which again is subjected to more detailed examination with the simulation model.
 
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