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About

Zhaofang Mao is an emerging researcher whose work sits at the dynamic intersection of industrial engineering, smart manufacturing, and human-robot collaboration (HRC). With a focused research portfolio centered on assembly line balancing, Mao has made significant contributions to understanding how collaborative robots can be strategically integrated alongside human workers to optimize production efficiency. Their most-cited work, "Balancing U-type assembly lines with human–robot collaboration" (2023, 36 citations), established foundational frameworks for deploying HRC in U-shaped assembly configurations, while subsequent studies extended this thinking to parallel assembly lines and multi-type robot scenarios. Collectively, these papers have accumulated over 80 citations in just two years, reflecting rapid recognition within the manufacturing systems community. Mao's research is particularly timely, addressing one of modern industry's most pressing challenges: how to harness automation without displacing human capability, but rather complementing it. By developing mathematical models and metaheuristic approaches such as tabu search, Mao bridges theoretical optimization with practical shop-floor applications, making their work valuable to both academic researchers and industry practitioners navigating the transition toward intelligent manufacturing environments.