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. Create a ‘Custom experiment’ in AnyLogic.Agents are identified and processes are modeled. The implementation of this simulation optimization framework in AnyLogic was done in the following way:Īs a first step, a simulation is created. It contains of three steps: (i) running the simulation with all transshipments (blue) points open (ii) running an optimization procedure to find the most promising ones (iii) presenting the solution.

The following video gives a short examples. Furthermore, routing and GIS integration is facilitated. To accomplish this, the system runs a Tabu Search, which uses an agent-based simulation to evaluate solutions and model randomness. Consequently, the solution procedures have to find the best set of points considering various uncertainties in the system. Transshipment of goods can only be performed at one of the blue dots, however, due to operational constraints, only a limited number of transshipment points can be operated simultaneously. Each shipment originates north of the river and has to be brought to a store south of the river, denoted in green. It assumes random and dynamic demand to be served by relief forces. Let’s refer to the following disaster relief setting. This post provides an example of such a combination with AnyLogic. I mostly use the simulation software package AnyLogic to accomplish this, which allows one to add optimization procedures coded in Java. It is a powerful tool to investigate complex setting with different input and influencing factors. Over the last years, I have worked on various research projects requiring the combination of simulation and optimization methods.

Anylogic software memory