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About

Samuel Moniz is a researcher whose work sits at the intersection of production planning, scheduling, and advanced manufacturing systems, with a particular focus on the integration of optimisation and simulation methodologies. His research addresses the growing complexity of modern industrial environments, including human–robot collaborative assembly lines and reconfigurable manufacturing systems shaped by the Industry 4.0 paradigm. Moniz's most influential contribution is his development of the Recursive Optimisation-Simulation Approach (ROSA), a two-level iterative methodology that bridges production planning and scheduling decisions in real industrial contexts. This work, published in 2021, has garnered 54 citations, reflecting its practical relevance and methodological novelty. His earlier simulation-optimisation framework for automated assembly lines operated by mobile robotic resources, published in 2018, laid important groundwork for this direction. In 2019, he extended his expertise to reconfigurable assembly lines, proposing a hybrid optimisation approach capable of handling dynamic demand volumes and varied product mixes — critical challenges in contemporary manufacturing. Across his body of work, Moniz consistently demonstrates a commitment to developing decision-support tools that translate rigorous quantitative methods into actionable industrial solutions, making his research valuable for both academic audiences and manufacturing practitioners navigating increasingly automated production environments.