Furthermore, you can output results and present statistics in multiple ways. The next run of the simulation automatically presents the optimization results to the user. Re-run the simulation with the adjusted input.With more recent versions of AnyLogic (starting from AnyLogic 7.2), this changes can be further directly implemented in the built-in database. At the start of a run, the simulation checks which points are active. In the presented case, open hubs are indicated with a binary variable, set and updated by the optmization procedure. Return the results of the optimization procedure to AnyLogic.See the AnyLogic API reference on class ExperimentCustom for additional implementation guidance. To enable such computations efficiently, make sure to increase memory and run simulation replications in parallel. The custom experiments changes input parameter, runs the simulation multiple times and returns the solution value of a setting. It uses multiple simulation runs to evaluate a single setting. In our case, we use a metaheuristic solution procedure based on Tabu Search. Therefore, the optimization procedure can be coded directly within the custom experiment or called externally. ![]() ![]() To integrate the optimization procedure, a custom experiment is created.
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