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!
Walmart, the world’s largest seller of groceries, recently began piloting first-of-a-kind automation for its popular online grocery pickup service.
Our partners at MOSIMTEC used AnyLogic to develop the simulation model behind the system’s development. Read on for more about the material handling system, videos of the robots in action, and MOSIMTEC's case study.
Being #26 in last years’ Fortune 500 list, Cardinal Health is a billion dollar pharmaceutical distribution and logistics company. Its clients are hospitals, pharmacies, physicians, and individual consumers. They face a multitude of typical distribution warehouse challenges that are further complicated by the nature of pharmaceutical products, which are smaller in size, consumable, expensive, and could be life critical. Company’s warehouses have narrow passages, and workers operate big multilevel trolleys. That increases the probability of employees’ mistakes and makes these mistakes costly.
This short tutorial shows how to build a simulation model based on a real-world problem description, using an example of a simple warehouse unloading process model. Also, it teaches to set animation and choose what-if scenarios to test. The AnyLogic User Support Team created the tutorial. The model simulates the arrival of trucks with two types of cartons to a warehouse. Workers unload the cartons, which then move on conveyors to the pallet stacking zone. After palletizing, the goods are moved by forklift trucks to the storage zone. Download the tutorial and accompanying material via our website.
There has been a dramatic increase in investment by both venture capital and strategic investors in new robotics technologies for supply chain automation. These investments have been driven by rapid changes in expectations for consistent, fast, flexible consumer experience across all channels. Traditionally, automation and associate order fulfillment software has been optimized around a limited set of products (or SKU’s) and for the requirements and constraints of a specific channel between manufacturer and consumer. This may result in different, and potentially incompatible technical solutions being implemented at the same distribution center. The new expectation is that the retailer provide a consistent, and hopefully superior, consumer experience, regardless of which channel is most convenient for the consumer to use.
Kuehne+Nagel, a leading global provider of logistics solutions assisted a large warehouse in finding the best algorithm for multi-order picking. The large warehouse processes approximately 13,000 orders or 750 picking cartons per day. Eight cartons are positioned on each trolley, but only four can be filled simultaneously due to weighing scales used to increase order picking accuracy. This and random carton placement along the pickers route means a strict algorithm is necessary for building optimal picking tours.