Tradeoffs between objective measures and execution speed in Iterative Optimization-based Simulation (IOS)

In this paper an Iterative Optimization-based Simulation (IOS) framework is designed, developed and examined. This framework includes a threefold contribution of Simulation, Optimization and Database Manager. In this IOS model, optimization takes place repeatedly at the model´s operational level to optimize the combination of system´s state variables during the simulation run. Predefined trigger events momentarily pause the simulation and activate optimization in order to optimize the system´s configuration. This cycle replicates until the simulation reaches its timespan. By deploying this promising IOS model, practitioners can take advantage of a long-term simulation run of their system, while it optimizing several times according to the occurrence of predefined incidents. The main concern here is the trade-off between simulation and optimization which is examined in this study. The results prove a positive impact of the IOS approach on the system´s performance measures, although it takes longer to execute compared to the Non-IOS approaches.