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Product Delivery Reinforcement Learning


Product Delivery Reinforcement Learning

Accenture partnered with San Francisco based AI company Pathmind to investigate the potential of new reinforcement learning (RL) opportunities in simulation.

The results obtained were extremely good. The method produced a waiting time more than 4x shorter than the Nearest Agent heuristic.

In this blog post, Agustin Albinati summarizes the model, introduces the three key considerations when defining the neural net, and presents the results of his team's investigations. Linked at the end of the blog post is a step by step how-to with Pathmind. Read on!

AnyLogic Cloud API: Python


AnyLogic Cloud API: Python

Introducing the AnyLogic Cloud API and Python. In this blog, see how to use the AnyLogic Cloud API with Python and evaluate its capabilities with an example model.

Python’s popularity ranks just behind that of JavaScript as the world’s second most popular language on GitHub. It is a popular language for machine learning, data processing, and data presentation. For AnyLogic, the Pypeline connector library allows you to call Python from within a running AnyLogic simulation model — learn more in our Pypeline webinar video. In this blog we will focus on the AnyLogic Cloud API and Python.

Restricted Areas: How to control access for transporters (Part 6)


Restricted Areas: How to control access for transporters (Part 6)

A key part of the AnyLogic 8.6 update related to the Material Handling Library. Now the movement of transporters such as AGV can be restricted by area and access can be permitted conditionally: by transporter number, by schedule, by throughput, and more.

This technical blog guides you through how to use these restricted areas and demonstrates them with the help of a practical example model: Areas with Limited Access for Transporters.

Network port: Learning to use the Material Handling Library (Part 5)


Network port: Learning to use the Material Handling Library (Part 5)

Part five of our series on AnyLogic’s Material Handling Library, a library specifically designed to help model the work of factories, warehouses, and other facilities where goods are moved, processed, and sorted.

This blog demonstrates how to connect individual conveyors, and any processing stations on them, into a single conveyor network using the Network Port element.

Ready to level up? Then let's dive right in!

How to build a stunning simulation


How to build a stunning simulation

The day AnyLogic presented their AnyLogic 8.5 updates I was very excited. In addition to the headline developments I found that they had added a 3D bike object. That reminded me of an old model of bikes that I had uploaded to RunTheModel, and that, in turn, made me think about developing an idea to create a similar model using the process modeling library. Would it be possible to create a discrete-event (DE) model that seems as if it was created entirely using agent-based (AB) modeling?

Webinar: Fundamentals of the AnyLogic Material Handling Library


Webinar: Fundamentals of the AnyLogic Material Handling Library

Learn how to model multi-level environments and how to simulate automated guided vehicles and cranes in this webinar video recording with supporting materials.

Using four example models, our in-house simulation expert and head of training in North America, Dr. Arash Mahdavi, introduces the fundamentals of the AnyLogic Material Handling Library. Understand the possibilities the library presents and see how to get started.