题目：矩和Wasserstein模糊下分布鲁棒优化的计算高效逼近

 Computationally Efficient Approximations for Distributionally Robust Optimization under Moment and Wasserstein Ambiguity

演讲人：潘凯 副教授 香港理工大学

时间：2022年10月21日上午 10:00

地点：腾讯会议

会议号：979 580 193

会议密码：239330

讲座摘要:

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 compatible with the available partial information. Although DRO bridges the gap between stochastic programming and robust optimization, one of its limitations is that its models for large-scale problems can be significantly difficult to solve, especially when the uncertainty is highly-dimensional. In this talk, we propose computationally efficient inner and outer approximations for DRO problems under a piece-wise linear objective function and with a moment-based ambiguity set and a combined ambiguity set including Wasserstein distance and moment information. In these approximations, we split a random vector into smaller pieces, leading to smaller matrix constraints. In addition, we use principal component analysis to shrink uncertainty space dimensionality. We quantify the quality of the developed approximations by deriving theoretical bounds on their optimality gap. We display the practical applicability of the proposed approximations in a production-transportation problem and a multi-product newsvendor problem. The results demonstrate that these approximations dramatically reduce the computational time while maintaining high solution quality. The approximations also help construct an interval that is tight for most cases and includes the (unknown) optimal value for a large-scale DRO problem, which usually cannot be solved to optimality (or even feasibility in most cases).

演讲嘉宾简介:

Dr. Kai Pan is currently an Associate Professor in the Department of Logistics and Maritime Studies 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, 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. Dr. Pan was the first-place winner of the prestigious IISE Pritsker Doctoral Dissertation Award in 2017. His works have been supported by the Research Grants Council (RGC) of Hong Kong and the National Natural Science Foundation of China.