Yanli Gong

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About

Yanli Gong is a leading researcher in operations research and artificial intelligence, with a primary focus on job-shop scheduling and resource flexibility. Her work bridges traditional optimization methods with cutting-edge AI-integrated approaches, addressing critical challenges in manufacturing and logistics systems. Gong’s most-cited paper, "Job-shop scheduling with resource flexibility: A systematic review from traditional to AI-integrated approaches" (2026), has already garnered 2 citations, establishing her as an emerging authority in this rapidly evolving field. This comprehensive review synthesizes decades of research, offering a roadmap for integrating machine learning and heuristic algorithms into flexible scheduling frameworks—a contribution that directly impacts real-world production efficiency and adaptability. Beyond this seminal work, Gong’s research explores how AI can enhance decision-making under uncertainty, with applications spanning smart manufacturing and supply chain management. Her systematic approach to reviewing and advancing scheduling methodologies has been recognized for its clarity and practical relevance, making her a sought-after collaborator in both academic and industrial settings. As a rising scholar, Yanli Gong continues to shape the future of intelligent scheduling, driving innovation at the intersection of operations research and artificial intelligence.