首页|Findings on Machine Learning Discussed by Investigators at University of Catania (Digital Twin Model With Machine Learning and Optimization for Resilient Produc tion-distribution Systems Under Disruptions)
Findings on Machine Learning Discussed by Investigators at University of Catania (Digital Twin Model With Machine Learning and Optimization for Resilient Produc tion-distribution Systems Under Disruptions)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news originating from Catania, Italy, by NewsRx c orrespondents, research stated, "Inspired by a real-life problem in the semicond uctor industry, we introduce a novel digital twin model for a company subject to the adverse effects of unpredictable disruptions. Specifically, this company ma nufactures a product using a raw material provided by an external supplier, whos e lead times may abruptly change due to disruptive events." Funders for this research include University of Catania, European Commission Joi nt Research Centre, Spanish Government. Our news journalists obtained a quote from the research from the University of C atania, "The Smoothing Order-Up-To rule is adopted by the company as a replenish ment policy. It is characterized by three control parameters, which must be opti mized to enhance the resilience of the system. To this end, the digital twin lea rns from the real production-distribution data and periodically self-adjusts the replenishment parameters based on the evolution of the external environment. Th e digital twin architecture combines data analytics, simulation modeling, machin e learning, and a metaheuristic. More specifically, an Artificial Neural Network learns from the manufacturer's operations and generates predictive models. Thes e are embedded in a Particle Swarm Optimization, which provides the optimal comb ination of the replenishment parameters. An experimental campaign was performed to demonstrate that the digital twin outperforms the traditional strategy in whi ch the replenishment parameters are kept unchanged."
CataniaItalyEuropeCyborgsEmergin g TechnologiesMachine LearningUniversity of Catania
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