Morrakot Raweewan

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About

Morrakot Raweewan is a researcher specializing in human-robot collaboration and intelligent manufacturing systems, with a particular focus on optimizing the integration of automation into assembly processes. Her work addresses one of modern manufacturing's most pressing challenges: how to effectively allocate tasks between human workers and robotic systems to maximize efficiency and ergonomic performance in semi-automatic assembly lines. Her most notable contributions center on developing systematic methodologies for human-robot task allocation in assembly line design. In her 2020 study on optimal task allocation, she pioneered a framework combining Design for Assembly (DFA) principles with optimization techniques, enabling manufacturers to evaluate task difficulty and make data-driven decisions about automation. Her subsequent 2021 work expanded this methodology, addressing the practical transition from lean manual assembly lines to semi-automated environments. With a growing citation record across her publications, Raweewan's research has attracted attention from both academic and industrial communities seeking practical solutions for Industry 4.0 transitions. Her interdisciplinary approach — bridging industrial engineering, robotics, and operations research — positions her as an emerging voice in smart manufacturing, offering manufacturers actionable frameworks for designing more efficient, human-centered production systems.