【学术会议】Data-Driven Robust Decision-Making
- 题目: Data-Driven Robust Decision-Making
- 报告人: Zhi CHEN, Imperial College Business School
- 时间: 4:00 pm – 5:00 pm, December 19, 2018
- 地点: Boardroom, Dao Yuan Building
In the era of modern business analytics, data-driven robust optimization (DDRO) has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set of the potential data-generating distributions and subsequently hedging against all member distributions within this ambiguity set, DDRO effectively combats the ambiguity that real-life data sets are plagued with. Amongst others, successful applications of DDRO have been reported in operations management, portfolio selection, energy systems, and medical decision-making.
In this talk, I propose two DDRO models where the ambiguity set comprises all distributions in a Wasserstein ball centered at the empirical distribution, thereby providing both finite sample guarantees and asymptotic consistency. My first model is a data-driven chance constrained program in which a decision has to be feasible with high probability under every distribution within the ambiguity set. My second model is a data-driven two-stage linear program that accounts for the deterministic immediate cost as well as the worst-case expected future cost. Both models are classical in the literature of decision-making under uncertainty and are NP-hard even if there is no ambiguity about the true data-generating distribution. I provide an exact deterministic reformulation for the first model, and I propose a high-quality randomized approximation scheme for the second model. The encouraging in-sample and out-of-sample performances, as well as the scalability of both models are demonstrated in numerical experiments on newsvendor, portfolio selection and transportation problems.
Zhi CHEN is a research associate at Imperial College Business School. He obtained a Bachelor of Engineering degree from Tsinghua University in China, and he holds a Ph.D. degree in Management from National University of Singapore. His research interests include (1) decision making under uncertainty with different levels of data availability and its applications in operations management, system control, and engineering; (2) cooperative game theory for joint activities and its applications in production economics and risk sharing. His work has been published in Operations Research and recognized by the finalist in the 2017 INFORMS George E. Nicholson Student Paper Competition.