Stochastic simulation models effectively capture complex system dynamics but are often too slow for real-time decision-making. Traditional metamodeling techniques learn relationships between simulator inputs and a single output summary statistic, …
Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to consider in this paper the ranking and selection problem in the presence of covariates, where the best alternative is not universal but depends on the …
We consider a ranking and selection problem in the context of personalized decision making, where the best alternative is not universal but varies as a function of observable covariates. The goal of ranking and selection with covariates (R&S-C) is to …
This paper considers the problem of ranking and selection with covariates (R&S-C), which is first introduced by Shen et al. (2017) and aims to identify a decision rule that stipulates the best alternative as a function of the observable covariates. We …
We consider a new ranking and selection problem in which the performance of each alternative depends on some observable random covariates. The best alternative is thus not constant but depends on the values of the covariates. Assuming a linear model …