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About

Yuexin Kang is a rising researcher in industrial engineering and operations management, specializing in the optimization of autonomous robotic systems for modern warehousing and e-commerce logistics. Their work focuses on enhancing order fulfillment efficiency through innovative algorithms and predictive models. Kang's most-cited paper, "Making better order fulfillment in multi-tote storage and retrieval autonomous mobile robot systems" (2024, 13 citations), addresses critical challenges in robotic warehouse coordination, demonstrating how intelligent storage and retrieval strategies can significantly improve throughput. Another key contribution, "Enhancing E-Commerce Warehouse Order Fulfillment Through Predictive Order Reservation Using Machine Learning" (2024, 6 citations), pioneers the use of machine learning to anticipate future orders and optimize online order batching—a largely overlooked area in both manual and robotic systems. Kang's recent work, "Warehouses with heterogeneous robots collaboration: operational policies and performance analysis" (2025), explores the complexities of deploying diverse robot fleets, offering actionable policies for seamless collaboration. With a growing citation record and a focus on bridging theoretical models with practical warehouse operations, Kang is establishing a reputation for advancing the frontier of intelligent, data-driven logistics.