Industry is increasingly striving to introduce concepts related to Industry 4.0. They are based on the new technologies, such as the Internet of Things, the cyber-physical systems and the big data analytics. Facing the challenges of introducing these concepts and technologies is becoming key to maintain the competitiveness of companies on the market. In the thesis we show the implementation of some aspects of Industry 4.0. First, we introduce the optimization of the production process using the automated guided vehicles. By simulating the production process, we find that we need four vehicles to replace two production workers. Economic analysis reveals that the investment would return in four years. The next goal of the thesis is to automate the assignment of machines to workers. We solve the problem with a combination of a greedy and a genetic algorithm and get better results compared to the manual assignment. We also show one of the possible uses of the digital twin concept in the production process. With the use of the machine learning we predict the performance of machines, which could allow us to preventively identify malfunctions.
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