Ranking and Contextual Selection
提出利用现成的仿真优化方法构建协变量与决策的数据库，形成分类器并给出最优性差距的上置信界，适用于个性化医疗和网页内容优化等无法实时仿真的场景。
Context-Sensitive Simulation-Based Decisions When There Is No Time to Simulate Stochastic simulation is a powerful tool for discovering system design decisions that are the best possible (optimal) when averaged over real-world uncertainty. However, in applications such as personalized medicine and web content optimization, even better decisions can be made if they are tailored to specific, contemporaneous covariate information, such as patient health history and user reading habits. Unfortunately, in these and similar applications, there is no time to perform a refined simulation optimization. In “Ranking and Contextual Selection,” Keslin, Nelson, Pagnoncelli, Plumlee, and Rahimian use off-the-shelf simulation optimization methods to create a database of covariates and associated decisions that form a covariate-to-decision classifier and an upper confidence bound on its optimality gap when applied to covariates not in the database. A realistic example of web page assortment optimization is presented using a data set from Yahoo!.