Kai Pan

Kai Pan is currently an Associate Professor in Operations Management at the Faculty of Business of The Hong Kong Polytechnic University (PolyU) and the Director of the MSc Program in Operations Management (MScOM). He received his Ph.D. and M.S. degrees from the University of Florida, USA, and his Bachelor's degree from Zhejiang University, China. Previously he worked as a Research Scientist at Amazon (Seattle, Washington) on Supply Chain Optimization and a Power System Engineer at GE Grid Solutions (Redmond, Washington) on Electricity Market Operations. His research interests include Stochastic and Discrete Optimization, Robust and Data-Driven Optimization, Dynamic Programming, and their applications in Energy Market, Smart City, Supply Chain, Shared Mobility, Marketing, and Transportation. His research on these topics has been published in Operations Research, Manufacturing and Service Operations Management, INFORMS Journal on Computing, Production and Operations Management, IISE Transactions, European Journal of Operational Research, IEEE Transactions on Power Systems, Transportation Research Part B, etc. All these journals are top-tier journals in various fields. Dr. Pan was the first-place winner of the prestigious IISE Pritsker Doctoral Dissertation Award in 2017.

Optimized Dimensionality Reduction for Moment-based Distributionally Robust Optimization

  • ShiyiJiang
  • Jianqiang Cheng
  • Kai Pan
  • Z.J. Max Shen
    • Categories Optimization in Data Science, Robust Optimization, Semi-definite Programming Tags , , , ,

    Moment-based distributionally robust optimization (DRO) provides an optimization framework to integrate statistical information with traditional optimization approaches. Under this framework, one assumes that the underlying joint distribution of random parameters runs in a distributional ambiguity set constructed by moment information and makes decisions against the worst-case distribution within the set. Although most moment-based DRO problems … Read more

    Computationally Efficient Approximations for Distributionally Robust Optimization

  • Jianqiang Cheng
  • Ruiwei Jiang
  • Meysam Cheramin
  • Kai Pan
    • Categories Stochastic Programming Tags , , ,

    Distributionally robust optimization (DRO) is a modeling framework in decision making under uncertainty where the probability distribution of a random parameter is unknown while its partial information (e.g., statistical properties) is available. In this framework, the unknown probability distribution is assumed to lie in an ambiguity set consisting of all distributions that are compatible with … Read more