博客

用人工智能和仿真技术解决工业问题


用人工智能和仿真技术解决工业问题

在Engineering Ingegneria Informatica (EII), 我们看到了将仿真与机器学习相集成的巨大潜力。这个简短的博客介绍了一个工业问题及其强化学习解决方案,由EII使用AnyLogic开发制作。AnyLogic的灵活性和可定制性使我们可以使用Skymind的外部深度强化学习包RL4J创建混合平台。

继续阅读,找出问题所在,并了解如何使智能体与AnyLogic环境进行交互,从而实现对智能体的训练。您还将学习如何以适合机器学习的方式解决工业问题。

How a digital twin can help decision making


How a digital twin can help decision making

Digital twins are part of Industry 4.0. How are they being used on automobile production lines? Here we investigate how a digital twin is made and used in the automotive industry.

One of the world’s largest capital goods companies, CNHi, wanted to evaluate Industry 4.0 technologies. It chose its IVECO production plant in Suzzara, Italy. Fair Dynamics were contracted and proposed a digital twin. Read on and discover how maintenance costs can be reduced with a digital twin — Production line downtime was shown in a study of over 100 automotive executives to cost an average of $22,000 per minute.

The development of Industry 4.0 in Germany: an interview with Yuri Toluyev


The development of Industry 4.0 in Germany: an interview with Yuri Toluyev

Industry 4.0 was the subject of Yuri Toluyev’s plenary presentation at the IMMOD simulation modeling conference in Saint Petersburg. He described how new approaches to enterprise development, united by the concept of Industry 4.0, are driving technological development.

AnyLogic attended the event and interviewed Toluyev about Industry 4.0, its development, and its implementation. Interesting and informative, read on for Yuri Toluyev’s Industry 4.0 insight...

Ford Motor Company Selects AnyLogic for their Simulation and Modeling Technology


Ford Motor Company Selects AnyLogic for their Simulation and Modeling Technology

We are excited to announce Ford Motor Company's recent selection of AnyLogic for their simulation and modeling needs. Ford Motor Company's substantial analytics team was looking for simulation modeling technology that goes above and beyond discrete-event only tools. The analytics team working with AnyLogic will provide decision-making and problem-solving techniques for multiple industries inside Ford, such as manufacturing, finance, and supply chain. AnyLogic is proud to support Ford Motor Company. Check out the press release announced on a variety media outlets.

anyLogistix Version 2.0, Released!


anyLogistix Version 2.0, Released!

We have released the second version of anyLogistix - a new tool for optimizing supply chains and logistics networks. What is anyLogistix? anyLogistix (ALX) - the only multimethod software for supply chain optimization. ALX combines traditional analytical methods of optimization and innovative simulation technology. The combination of different technologies allows you to model and analyze the supply chain, at any level of detail, therefore, finding more effective ways to improve. Who is anyLogistix made for? Companies with large and complex supply chains (i.e. manufacturers, distributors, retailers and logistics providers) can take advantage of anyLogistix. Implementation of ALX is carried out by our partners,consulting companies.

Automated Production Line Optimization: Case Study


Automated Production Line Optimization: Case Study

Centrotherm Photovoltaics AG is a global supplier of technology and equipment for the photovoltaics, semiconductor, and microelectronics industries. The company needed to identify the best configuration of the automated production line and factory, to minimize costs and maximize throughput and reliability. Throughput and equipment utilization rate metrics were utilized to compare alternatives. Centrotherm needed to avoid possible bottlenecks in the material flow and optimize in-factory logistics. Also, management required taking into account casualties and stochasticity, for instance, the probability of scrap or how the factory would operate in case of equipment breakdowns.

Quick Win for Negotiating with Supplier Using Simulation Results


Quick Win for Negotiating with Supplier Using Simulation Results

Equipment part breakdown in the Intel factories, as in many factories is inevitable. These failures typically cause capacity constraints and ultimatley cost the corporation time and money. Equipment parts can be repaired locally or may require shipping to the vendor for repair. Since the repair loop takes significant time, it is necessary to have extra spare parts on-hand to keep the equipment running while broken parts are repaired. It is pertinent to avoid overbuying of the spare parts, as they are very expensive. Intel needed a model of the repair loop to increase the visibility of problems such as broken parts accumulating at the vendor repair center and sites over purchasing spare parts. At the AnyLogic Conference 2014, Victor Chang, Software Engineer at Intel presents an AnyLogic simulation that was developed to model the complexities and variability

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