  This research considers the ranking and selection (R&S) problem of selecting
the optimal subset from a finite set of alternative designs. Given the total
simulation budget constraint, we aim to maximize the probability of correctly
selecting the top-m designs. In order to improve the selection efficiency, we
incorporate the information from across the domain into regression metamodels.
In this research, we assume that the mean performance of each design is
approximately quadratic. To achieve a better fit of this model, we divide the
solution space into adjacent partitions such that the quadratic assumption can
be satisfied within each partition. Using the large deviation theory, we
propose an approximately optimal simulation budget allocation rule in the
presence of partitioned domains. Numerical experiments demonstrate that our
approach can enhance the simulation efficiency significantly.
