Valery Kyrylovych

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About

Valery Kyrylovych is a researcher specializing in the automation of fuzzy multi-criteria decision-making, with a particular focus on robotic machine-assembly technologies. His major contribution lies in developing a novel method for selecting optimal robotic assembly technologies using a worst-case approach, which enhances reliability in complex manufacturing environments. This work, detailed in his 2019 paper "Automation of fuzzy multi-criteria selection of robotic machine-assembly technologies using worst-case approach" (2 citations), introduces the original WMS (Worst Me) framework to automate the selection process under uncertainty. While his citation count is modest, Kyrylovych's research addresses a critical gap in industrial automation by providing robust solutions for multi-criteria problems where worst-case scenarios must be prioritized. His approach is particularly valuable for engineers and researchers working on adaptive manufacturing systems, offering a practical tool for improving decision-making in robotics. Kyrylovych's work exemplifies the application of fuzzy logic to real-world engineering challenges, making him a notable contributor to the field of automated assembly technologies.