In this paper, we briefly review the development of ranking and selection (R&S) in the past
70 years, especially the theoretical achievements and practical applications in the past 20 …

We consider a problem of ranking and selection via simulation in the context of personalized
decision making, in which the best alternative is not universal, but varies as a function of …

We derive a new optimal sampling budget allocation for belief models based on linear
regression with continuous covariates, where the expected response is interpreted as the …

Optimization via simulation has been well established to find optimal solutions and designs
in complex systems. However, it still faces modeling and computational challenges when …

We consider the problem of selecting the best alternative in a setting where prior similarity
information between the performance output of different alternatives can be learned from …

We consider a simulation optimization problem for context-dependent decision-making,
which aims to determine the top-designs for all contexts. Under a Bayesian framework, we …

This paper considers the problem of ranking and selection with covariates and aims to
identify a decision rule that stipulates the best alternative as a function of the observable …

We provide a tutorial overview of recent advances in three methodological streams of
statistical literature: design of experiments, variable selection, and approximate inference …

In this work, we investigate the offline contextual learning in simulation optimization. In
offline contextual learning, the optimal design changes with the context. Therefore, the …

We consider a recently proposed simulation-based decision-making framework, called
offline-simulation-online-application (OSOA). In this framework, simulation experiments are …