To build a simulation that closely reflects a real-world facility, you might need to create large-scale networks. Those are complex internal logistics systems usually found in ports, terminals, warehouses, and factories. In AnyLogic 8.8.5, working with such projects has become faster and smoother.
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AnyLogic Cloud 2.4.0 – optimization experiment at scale
The latest update from AnyLogic Cloud now supports an optimization experiment. This experiment type is one of the most complex and will be the focus of this blog post. Read on to find out how to prepare an AnyLogic 8 model for such an experiment, which can then be exported to AnyLogic Cloud. Then design an optimization experiment, run this experiment, and finally process the results, all in AnyLogic Cloud.
AnyLogic Cloud 2.3.3 – minor improvements precede major new features
AnyLogic Cloud’s product owner highlights main experiment dashboard charts improvements. A scatter plot can be used for model input-output dependency, a mean and error bar is added to the variation with replication experiment and can be labeled with time or date, a specific time unit can be chosen, a box plot shows mean values, and a time color chart has better validation and color assignment. Learn more in the blog post.
Streamlining the connection to trained ML models with the ONNX Helper Library
There are many cases where it’s desirable to incorporate trained machine learning (ML) models into a simulation model. Now, thanks to a new AnyLogic library, using ONNX ML models is easier and more efficient.
By incorporating this add-on library into your AnyLogic environment, your models can access its functionalities, just like with any other built-in library. It’s simply a matter of adding the helper object to your model and configuring it. Read on to find out more.