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 with the empirical optimization model, as a trade-off 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. We incorporate this framework on discrete choice model, which is a key focus in transportation. We show that via robust satisficing, the assortment optimization problem with distributional ambiguity can be tractable and lead to solutions with appealing performances.
Daniel Zhuoyu LONG is an associate professor at the Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong (CUHK). Before joining CUHK, he received his Ph.D. degree from the Department of Analytics and Operations at the National University of Singapore in 2013, his master degree from the Chinese Academy of Science in 2008, and his bachelor degree from Tsinghua University. His research interests revolve around distributionally robust optimization, risk management, and broad applications in supply chain management, revenue management, and project management. His papers have been awarded as a finalist in the best OM paper in OR 2021 and the first prize in the Best Paper Award at the CSAMSE conference 2022.