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About

Robert J. Schlesinger is a pioneer in the application of quantitative decision-making models to manufacturing and robotics. His research centers on the critical challenge of robot selection, where he developed and compared statistical techniques—such as ordinary least squares and linear goal programming—to help technical managers navigate the complexity of nonuniform performance specifications. His most influential work, "Decision Models for Robot Selection: A Comparison of Ordinary Least Squares and Linear Goal Programming Methods" (1989), has garnered 66 citations, underscoring its lasting impact on industrial engineering and operations research. Schlesinger also contributed to the foundations of data collection in computer-integrated manufacturing, applying the sampling theorem to improve factory floor information systems. His early investigation into robot performance specifications (1986) remains a key reference for understanding the difficulties posed by a lack of standardization in the rapidly evolving robotics field. Through his methodical, data-driven approach, Schlesinger provided engineers and managers with rigorous tools to make informed, cost-effective automation decisions—work that continues to inform modern manufacturing strategy and robotic system design.