演讲人: 龙卓瑜副教授，香港中文大学

时间: 2021年10月28日上午 10:00

地点: 腾讯会议会议号：889 647 645 会议密码：380118

讲座摘要:

We present a general framework for robust satisficing that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution deviates from the empirical distribution. The satisficing decision maker specifies an acceptable target, or loss of optimality compared to the empirical optimization model, as a tradeoff for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with robust satisficing, termed as the fragility measure, and present its representation theorem. Focusing on Wasserstein distance measure with i1-norm, we present tractable robust satisficing models for risk-based linear optimization, combinatorial optimization, and linear optimization problems with recourse. Serendipitously, the insights to the approximation of the linear optimization problems with recourse also provide a recipe for approximating solutions for hard stochastic optimization problems without relatively complete recourse. We perform numerical studies on a portfolio optimization problem and a network lot-sizing problem. We show that the solutions to the robust satisficing models are more effective in improving the out-of-sample performance evaluated on a variety of metrics, hence alleviating the Optimizer’s Curse (Smith and Winkler 2006).

演讲嘉宾简介:

Daniel Zhuoyu Long is an associate professor in the Department of Systems Engineering & Engineering Management at The Chinese University of Hong Kong. He earned his PhD in the Department of Analytics and Operation, National University of Singapore in 2013. Before that, Long received his bachelor degree from Tsinghua University, and master degree from Chinese Academy of Science. His research interests are in inventory control, project management, risk management, and robust optimization.