Learning-augmented optimisation for transportation, supply chain, and service systems

This talk introduces my research programme at the intersection of optimisation, stochastic modelling, and data-driven methods for complex service and supply chain systems. Across my work, I study how learning can be combined with structured operations models to improve scalability, capture real-world behaviour, and preserve decision reliability. I will highlight several threads of this agenda, including robotic warehouse operations and design, large-scale freight network modelling and disruption analysis, and healthcare logistics. Although these applications differ in scale and context, they share a common methodological philosophy: learn what is difficult to model directly, retain the underlying decision structure, and validate approximations carefully before optimization. My goal is to build practical, rigorous tools for the design and operation of large-scale socio-technical systems.

Huang Yiduo is a Postdoctoral Research Associate in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, working with Prof Yanfeng Ouyang. He received his PhD in Civil and Environmental Engineering from the University of California, Berkeley in 2024, advised by Prof Zuojun Max Shen, and his BSE from Tsinghua University. His research lies at the intersection of optimisation, stochastic modelling, and data-driven methods for transportation, logistics, and supply chain systems, with recent work on intermodal freight networks, robotic warehouses, and healthcare operations.

For more information about the ESD Seminar, please email esd_invite@sutd.edu.sg