AnyLogic User Experience session videos: Material Handling Library developer showcase of current and upcoming features, including demos from John Deere and Tata Steel. Watch the videos and learn more!
ITC Infotech undertook work to optimize inventory keeping in complex asset intensive industries. By combining simulation, machine learning, and optimization, they demonstrated effective asset management and inventory optimization for rotable/repairable spares that balances service levels and inventory costs.
See how Kumar Sumit and his team at ITC Infotech used OptQuest optimization, Python, decision tree machine learning, and AnyLogic for repairables asset management optimization.
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.
Manufacturers are finding that the new normal is an increased need to quickly adapt. As the COVID-19 pandemic continues to cause disruptions, Steve Sashihara and Patricia Randall of Princeton Consultants have reported on how they are using simulation modeling to help companies develop, optimize, and communicate new manufacturing policies.
As one of the largest steel manufacturers and suppliers in the world, Tata Steel faced the need to optimize the internal logistics of one of its steel manufacturing units. The company saw potential for increasing the unit’s overall throughput using AnyLogic as production optimization software.
Read on and find out more about the case.
Learn how to create a self-configuring material handling digital twin from this webinar recording with accompanying simulation model. Presented by Dr. Benjamin Schumann in three chapters, the webinar covers how to automatically configure machines and products, conveyors, and automatic guided vehicles (AGV).
Imagine receiving a call from Jeff Bezos — he wants you to simulate a...
AnyLogic experts at business consulting company NFP worked to resolve a crane task distribution problem for a major international metallurgical company. Their innovative solution employed machine learning and improved on an expert selected policy. Read on to learn more about the project.
The simulation model can be found in the cloud and was featured as model of the month December 2019.
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.
AnyLogic Help has a new tutorial: Lead Acid Battery Production (Material Handling). By following the tutorial, you can learn how to model material handling processes using AnyLogic’s specialized Material Handling Library. The tutorial explains step-by-step how to create a model of a lead-acid battery production line. The model includes path-guided and free-space automatic guided vehicles (AGV), conveyors, and cranes. Check it out!
A short blog presenting an industrial problem and its reinforcement learning solution, made using AnyLogic and developed by EII. The flexibility and customizability of AnyLogic allowed the use of RL4J to create a hybrid platform.
Read on, find out about the problem and see how to train learning agents by letting them interact with an AnyLogic environment. You will also learn the techniques used to formulate the industrial problem in a way fit for machine learning.