Yongzhi Qi (戚永志)

Technical Director of the Supply Chain Algorithm Team at JD.com (JINGDONG)
Guest Professor at the University of Hong Kong
Committee member of the Data Science and Operations Intelligence Branch of the Operations Research Society of China

I obtained my Ph.D. from Shandong University in 2016, then did postdoctoral research at the China Electric Power Research Institute. During this time, I also spent some time as a visiting scholar at Stanford University, focusing on optimizing water supply systems. In 2018, I joined JD.com, where I’ve been working on supply chain optimization ever since.

My work spans various aspects of the supply chain, such as assortment planning, pricing, inventory management, network planning, and fulfillment. I lead some key projects funded by China’s National Ministry of Science and Technology, collaborating with businesses and research institutions to create an intelligent supply chain AI platform.

I’ve published in journals like Management Science, Production and Operations Management Society (POMS), Manufacturing & Service Operations Management (M&SOM), and IEEE Transactions. It is with great honor that I share my team and I have been recognized with several prestigious awards, including being finalists for the INFORMS Franz Edelman Award and winning awards like the INFORMS Prize and Gartner’s Technology Innovation Award.

Professional Activities and Awards

Associate editor for Asia-Pacific Journal of Operational Research
Daniel H. Wagner Prize（2024）
Gartner Power of the Profession Supply Chain Awards（2024）
INFORMS Prize（2024）
Finalist, Franz Edelman（2023）
Finalist, M&SOM Practice-based Research Competition（2023）
Semifinalist, Gartner Power of the Profession Supply Chain Award (2022)

Publications

Shen, Z. J. M., Sun, S., Qi, Y. *, Hu, H., Kang, N., Zhang, J., Wang, X., & Lin, X. (2025). JD.com Improves Fulfillment Efficiency with Data-driven Integrated Assortment Planning and Inventory Allocation. INFORMS Journal on Applied Analytics. Available at arxiv:2509.12183.
Qi, Y., Yin, J., Zhang, J., Geng, D., Chen, Z., Hu, H., Qi, W., & Shen, Z.J. M. (2025). Leveraging LLM-Based Agents for Intelligent Supply Chain Planning. Available at arXiv:2509.03811.
Hu, H., Wu, Y., Shi, Z., Qi, Y. *, Zhang, J., Han, S., … Wang, L. (2025). Explainable probabilistic forecasting for time series in supply chains: a latent auto-encoder approach. International Journal of Production Research, 1–21. https://doi.org/10.1080/00207543.2025.2541860
Lee, H. L., Shen, Z. J. M., Qi, Y., & Chen, Z. (2025). AI-driven Supply Chain Transformation. Available at SSRN:5244331.
Qi, Y., Hu, H., Lei, D., Zhang, J., Shi, Z., Huang, Y., … & Shen, Z. J. M. (2025). TimeHF: Billion-Scale Time Series Models Guided by Human Feedback. Available at arXiv:2501.15942.
Chi, Y., Lei, D., Qi, Y., Zhang, J., Hu, H., Zheng, L., & Shen, Z. J. M. (2024). Bridging Historical Data and Future Markets: An Optimal Transport Policy for Demand Forecasting. Available at SSRN:4990206.
Hu, H., Qi, Y. *, Lee, H. L., Shen, Z. J. M., Liu, C., Zhu, W., & Kang, N. (2024). Supercharged by Advanced Analytics, JD.com Attains Agility, Resilience, and Shared Value Across Its Supply Chain. INFORMS Journal on Applied Analytics, 54(1), 54–70.
Lei, D., Qi, Y. * , Liu, S. * , Geng, D., Zhang, J., Hu, H., & Shen, Z. J. M. (2024). Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach. Manufacturing & Service Operations Management.
Yin, J., Shi, Z., Zhang, J., Lin, X., Huang, Y., Qi, Y., & Qi, W. (2024). sTransformer: A Modular Approach for Extracting Inter-Sequential and Temporal Information for Time-Series Forecasting. Available at arXiv:2408.09723.
Lei, D., Hu, H., Geng, D., Zhang, J., Qi, Y., Liu, S., & Shen, Z. J. M. (2023). New Product Life Cycle Curve Modeling and Forecasting with Product Attributes and Promotion: A Bayesian Functional Approach. Production and Operations Management, 32(2), 655–673.
Qi, M., Shi, Y., Qi, Y., Ma, C., Yuan, R., Wu, D., & Shen, Z. J. (2023). A Practical End-to-End Inventory Management Model with Deep Learning. Management Science, 69(2), 759–773.
Chen, N., Kang, W., Kang, N., Qi, Y., & Hu, H. (2022). Order Processing Task Allocation and Scheduling for E-Order Fulfilment. International Journal of Production Research, 60(13), 4253–4267.
Sun, Y., Shi, Z., Zhang, J., Qi, Y., Hu, H., & Shen, Z. M. (2022). Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting. Available at arXiv:2212.06620.
Wang, L., Deng, T., Shen, Z. J. M., Hu, H., & Qi, Y. (2022). Digital Twin-Driven Smart Supply Chain. Frontiers of Engineering Management, 9(1), 56–70.
Lin, X., Zhang, B., Zhang, J., Qi, Y., & Hu, H. (2021). A Practical Framework for Forecasting Stock Keeping Unit Level Seasonal Sales. In 2021 7th International Conference on Big Data and Information Analytics (BigDIA) (pp. 393–398). IEEE.
Qi, Y., Huang, Y. H., Wang, W. S., Wang, Y. F., Pang, X. Y., Zhang, C., & Zhang, H. T. (2020). A Study on Hydro-Wind-Solar Consumption Analysis Method for High Proportion of Clean Energy. Power System and Clean Energy, 36(1), 55–63.
Wu, Q., Zhang, D. X., Ling, X. F., Liu, D. W., Qi, Y., Ma, S. Y., & Zheng, C. Y. (2019). Dynamic Analysis of Disturbance Propagation in Power Grid Based on an Epidemic Model. Proceedings of the CSEE, 39, 4061–4069.
Qi, Y., Liu, Y., & Wu, Q. (2017). Non-Cooperative Regulation Coordination Based on Game Theory for Wind Farm Clusters During Ramping Events. Energy, 132, 136–146.
Qi, Y., Zhang, N., Huang, Y., Wang, Y., Wang, W., & Liu, C. (2017). Wind Power Curtailment Sequence Characteristic Analysis. The Journal of Engineering, 2017(13), 1662–1665.
Qi, Y., & Liu, Y. (2016). Wind Power Ramping Control Using Competitive Game. IEEE Transactions on Sustainable Energy, 7(4), 1516–1524.
Qi, Y., & Liu, Y. (2015). Wind Farms Coordination Control Based on Contribution Index. In 2015 IEEE Power & Energy Society General Meeting (pp. 1–5). IEEE.
Qi, Y., & Liu, Y. (2014). Output Power Rolling Optimization and Real-Time Control in Wind-Photovoltaic-Storage Hybrid System. Transactions of China Electrotechnical Society, 29(8), 265–273.
Qi, Y., & Liu, Y. T. (2014). Ramping Coordination Control of Wind Generation Based on Competitive Game Theory. Proceedings of the CSEE, 34(25), 4341–4349.
Qi, Y., & Liu, Y. (2013). Wind Generation Ramping Coordinated Control. In 2nd IET Renewable Power Generation Conference (RPG 2013) (pp. 3–C08). IET.
Qi, Y., & Liu Y. (2013). Finite Control of High Risk Wind Power Ramping. Proceedings of the CSEE, 33(13), 69–75.
Xia, X., Qi, Y., & Liu, Y. (2013). Variable-Speed Variable-Pitch Coordinated Control Strategy of Wind Turbine Ramping Power. In 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) (pp. 1–5). IEEE.
Qi, Y., & Liu, X. (2012). Wind-Photovoltaic-Storage System Optimal Control Scheme Based on Generation Scheduling. In IEEE PES Innovative Smart Grid Technologies (pp. 1–4). IEEE.
Qi, Y., & Liu, Y. (2012). Finite Control on Wind Power Ramping Event. In 2012 10th International Power & Energy Conference (IPEC) (pp. 655–658). IEEE.

* Corresponding Author