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自动导向车辆被广泛用于制造和仓储以移动物品。一个简单的示例模型结合了各种所有图表和建模方法，以模拟AGV充电过程。查看文章的同时不要忘记在AnyLogic中打开模型以遵循该示例。

我们很自豪地推出了一项期待已久的新功能——可逆式输送带。另外，AnyLogic 8.7.12还内置了重新设计的AnyLogic帮助。继续阅读了解更多信息。

AnyLogic 8.6更新的关键部分与物料处理库有关。现在，诸如AGV之类的运输工具的移动可以受到区域的限制，并且可以有条件地允许进入：通过运输车数量，计划，吞吐量等等。对于制造和仓库设置，可以以多种方式应用此功能，包括对门进行建模，限制操作机械（例如起重机）时的限制，以及避免在装货区域或交叉路口造成拥挤。

通过此网络研讨会视频回放以及随附的仿真模型，学习如何创建自配置物料搬运数字孪生。此次网络研讨会由本杰明·舒曼（Benjamin Schumann）博士分三部分进行介绍，涵盖了如何自动配置机器和产品、输送机和自动导引车（AGV）。

想象一下您接到了马云的电话——

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.