Maryam Mousavi

Papers

Total Citations

210

H-Index

About

Maryam Mousavi is a researcher specializing in intelligent manufacturing systems, metaheuristic optimization, and robotics, with a particular focus on advancing automation in flexible manufacturing environments. Her most recognized contribution is her 2017 work on multi-objective automated guided vehicle (AGV) scheduling within flexible manufacturing systems (FMS), which has garnered over 135 citations and remains a landmark reference in the field. In this study, she developed a hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) approach that significantly improved scheduling efficiency by balancing competing objectives such as cost and operational time. Complementing this, her fuzzy hybrid GA-PSO algorithm further refined AGV scheduling under uncertainty, accumulating an additional 50 citations and demonstrating her commitment to robust, real-world applicable solutions. Beyond scheduling, Mousavi has explored robotic systems optimization, notably investigating how virtual reality environments can be leveraged to minimize robot arm movement time, enhancing production throughput in industrial settings. Across her body of work, Mousavi has established herself as a creative problem-solver at the intersection of artificial intelligence and smart manufacturing, offering practical tools that help industries become more agile, efficient, and responsive to dynamic production demands.