Reporter：Siyang Gao
Abstract：The ranking and selection (R&S) problem seeks to efficiently select the best simulated system design among a finite number of alternatives. It is a well-established problem in simulation-based optimization. In this research, we consider R&S in the presence of context, where the context corresponds to some side information to the simulation model. We utilize the OCBA approach to formulate the problem, design algorithms and conduct theoretical analysis. The performance of the proposed algorithm is demonstrated via a set of abstract and real-world problems.
