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About

Nourma Khader is a leading researcher at the intersection of healthcare automation, operations research, and artificial intelligence. Her work focuses on optimizing robotic dispensing systems (RDSs) for high-throughput mail-order pharmacy automation (MOPA) facilities, where she addresses critical challenges in medication planogram design and replenishment logistics. Khader’s most influential paper, “Pharmacy robotic dispensing and planogram analysis using association rule mining with prescription data” (41 citations), pioneered the use of data mining to uncover medication co-dispensing patterns, enabling more efficient robotic workflows. She advanced this with a multi-objective optimization framework that integrates association rule mining with evolutionary algorithms (9 citations), allowing facilities to balance robot utilization, prescription throughput, and inventory costs. Her robust receding horizon control strategy for replenishment planning (12 citations) further ensures real-time adaptability in dynamic pharmacy environments. By combining prescriptive analytics with robotic automation, Khader’s work directly impacts operational efficiency in high-volume fulfillment centers, reducing waste and improving patient access to medications. Her research is essential reading for scholars in healthcare logistics, industrial engineering, and AI-driven automation, offering scalable solutions for the future of pharmacy dispensing.