Krishnaswami Srihari

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About

Krishnaswami Srihari is a leading researcher in healthcare automation and operations research, with a focus on optimizing robotic dispensing systems for high-volume pharmacy fulfillment. His work addresses critical challenges in mail-order pharmacy automation (MOPA) facilities, where efficient medication planogram design and replenishment are essential for throughput. Srihari’s major contributions include developing a robust receding horizon control strategy for replenishment planning, which dynamically manages inventory in robotic dispensing systems to minimize disruptions (12 citations). He also pioneered a multi-objective optimization framework that integrates association rule mining with evolutionary algorithms to solve complex planogram layout problems, enhancing both picking efficiency and storage utilization (9 citations). These innovations directly improve operational performance in large-scale pharmaceutical distribution, reducing costs and error rates. Srihari’s research bridges industrial engineering, data mining, and healthcare logistics, offering practical solutions for automated pharmacy systems. His work is particularly notable for its application of advanced control theory and combinatorial optimization to real-world automation challenges, making him a key figure in the emerging field of intelligent pharmacy robotics.