Papers
3
Total Citations
124
H-Index
3
About
Mark Goh’s research lies at the intersection of healthcare operations, robotics, and intelligent decision-making, with a focus on transforming hospital logistics through automation and digital innovation. His work on mobile robot selection for hospital pharmacies introduced a fuzzy extended VIKOR model, a multi-criteria decision-making framework that helps healthcare facilities choose optimal robotic systems for medication delivery—a critical step toward the Internet of Health Things. This paper, with 67 citations, has influenced how hospitals evaluate automation investments. Goh further advanced the field by applying digital twinning and robotic process automation to improve productivity in greenfield hospitals, demonstrating how simulation can optimize real-world workflows. His analysis of clustering methods for balanced multi-robot task allocation addresses the challenge of equitable task distribution among robots, minimizing travel time differences and improving overall system efficiency. By tackling both the selection and coordination of robotic systems, Goh has provided practical frameworks that bridge theoretical optimization with healthcare implementation. His work is essential reading for researchers and practitioners seeking to integrate autonomous systems into complex, high-stakes environments like hospitals.
Research Focus
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Top Papers
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