Problem: Vale needed to maximize productivity at its Moatize coal mines in Mozambique. The complex operations involved multiple mining fronts, varied coal types,... ... AnyLogic simulation model to replicate mine operations. The model accounted for 8 mining fronts, 80 trucks, crushers, and shift schedules while incorporating optimization data to balance coal transport capacity with waste removal constraints. Results: ...

... optimization experiment intended to find, for a fixed arrival rate, the optimal set of parameters to minimize the mean cost per product. Alpyne is used to train a reinforcement learning policy to optimally set the parameters for any arrival rate. Explore ... ... passport control and vehicle inspection at the crossing of an international border. The model is set up to allow overriding the schedule for the number of car and bus inspectors (two resource pools) for the duration of the model. The Python ...

... healthcare policy planning, the allocation and distribution of resources epidemic simulation, evaluating disease spread and mitigation strategies pharmaceutical supply chain simulation and production planning marketing and promotion in the pharmaceutical industry AnyLogic has a GSA Contract schedule #47QTCA18D007Q. Government agencies of the United States of America may purchase through GSA Advantage.

... manufacturing reliability and supply disruptions in the market due to FDA and DDA regulations. In summary, Cardinal Health must keep up with the variability in pharmaceutical distribution management. Cardinal Health considers facility layout, flow of product, order picking, labor planning & scheduling, customer order requirements and congestion for analysis and day to day operations management. Traditional analysis tools such as empirical trial and error, are risky, expensive and difficult to make changes. Industrial engineering operations ...

... turned to AI-powered simulation to deal with complex scheduling challenges. Using a genetic algorithm within AnyLogic, they replaced manual planning with an optimized, data-driven approach that significantly boosted efficiency. Using one of the job shop scheduling techniques – a genetic algorithm In another case study, Lagor improved production efficiency by combining a digital twin of their shop floor with deep reinforcement learning. Using AnyLogic, consultants trained an AI agent to optimize core movements and reduce bottlenecks across the manufacturing line. GSK took a different ...