Integrating Python with AnyLogic for Simulation Modeling
Python + AnyLogic
The Python API for AnyLogic provides a powerful way to take your simulation models to the next level.
AnyLogic, a leading simulation software for business applications utilized worldwide by over 40% of Fortune 100 companies, has recently integrated a Python API for AnyLogic to work on simulation models stored on your personal AnyLogic Cloud. This new functionality is expected to bring potential benefits to AnyLogic and Python users in the development, configuration and experimentation of their simulation models.
Python API for AnyLogic
AnyLogic simulation models stored in your personal AnyLogic Cloud account can be configured and run programmatically through a Python API for AnyLogic to obtain experimental results and evaluate multiple scenarios by changing parameters values.
This Python new functionality with AnyLogic represents a great tool for when required to run and experiment with AnyLogic simulation models in a computer that do not has the AnyLogic software installed.
To start using the Python API for AnyLogic, install the AnyLogic cloud client library using pip package installer:
Minimalistic Simulation Model
The model analyzed in this tutorial (i.e. Service System Demo) can be found in AnyLogic cloud website.
The Service System Demo model consists in a single source, a service module, a check point and one sink. The time agents spend in the system is measured and the goal is to obtain the mean total time agents spend in the system, the server utilization and mean queue size.’
Follow the next steps to run to run the Service System Demo model on AnyLogic Clod website:
- Type ‘Service Systems Demo’ in AnyLogic Cloud website search bar. The model will be displayed in the screen as follows:
- Click on the Service Systems Demo model to open it.
- Click play to run the simulation model and obtain the results.
According to the baseline model’s results, the mean total time in the system is 4.63, the server utilization 0.83 and the mean queue size 2.54.
Now, let us run the same simulation model using the Python API for AnyLogic by linking it with the AnyLogic model in the cloud. Follow the next steps to run to run the Service System Demo model:
Comparison with Baseline Model:
By increasing the server capacity from 3 to 8, the mean queue size was reduced from 2.54 to 1 agents. In addition, the mean server utilization was also reduced from 0.83 to 0.31, representing additional idle time that should be further optimized.
What will come next?
Python’s new capabilities with AnyLogic are expected to increase its functionality for simulation projects. While its current applications are still limited, we can expect new functionalities in the near future considering Python’s popularity between programmers and engineers. For help on the Python API for AnyLogic click here to schedule a call.
Interested in obtaining an AnyLogic license? Click here to schedule a call.
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