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About

Berna Dengiz is a leading researcher in manufacturing systems optimization, with a primary focus on flexible manufacturing systems (FMS) and decision support methodologies. Her work bridges industrial engineering and artificial intelligence, particularly through the development of a decision support system for robot selection—her most-cited paper with 77 citations, which provides a structured framework for choosing industrial robots in complex production environments. Dengiz has made significant contributions to understanding manufacturing flexibility, including her innovative definition of "machine sequence flexibility" as a combination of operation and routing flexibilities, and her analysis of how these factors impact system performance. She also advanced multi-objective optimization by combining design of experiments (DOE) with goal programming approaches to analyze robustness in flexible manufacturing cells, ensuring operational stability under varying conditions. Her research is notable for integrating statistical methods with practical manufacturing challenges, offering tools that improve efficiency and adaptability in production systems. Dengiz’s work continues to influence both academic research and industrial applications, providing foundational insights for students and practitioners seeking to optimize flexible automation and decision-making in modern manufacturing.