Shahabeddin Sotudian

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About

Shahabeddin Sotudian’s research lies at the intersection of fuzzy logic, operations research, and manufacturing systems, with a focus on optimizing complex scheduling problems under uncertainty. His most-cited work, “Fuzzy Gilmore and Gomory algorithm: Application in robotic flow shops with the effects of job-dependent transportation and set-ups” (2020, 4 citations), addresses a critical gap in flexible manufacturing: the often-overlooked impact of job-dependent transportation and set-up times on robotic cell scheduling. By integrating fuzzy methodology into the classic Gilmore-Gomory algorithm, Sotudian demonstrates that ignoring these parameters can lead to significant errors in production planning. His contribution provides a more realistic and robust framework for minimizing cycle times in robotic flow shops, where transportation and set-up constitute a large portion of total production time. This work is particularly valuable for industries relying on automated manufacturing cells, offering a practical tool to enhance efficiency under real-world vagueness. Sotudian’s research underscores the importance of bridging theoretical algorithms with operational realities, making his findings relevant for both scholars and practitioners in industrial engineering and fuzzy systems.