  We consider a simulation optimization problem for a context-dependent
decision-making. A Gaussian mixture model is proposed to capture the
performance clustering phenomena of context-dependent designs. Under a Bayesian
framework, we develop a dynamic sampling policy to efficiently learn both the
global information of each cluster and local information of each design for
selecting the best designs in all contexts. The proposed sampling policy is
proved to be consistent and achieve the asymptotically optimal sampling ratio.
Numerical experiments show that the proposed sampling policy significantly
improves the efficiency in context-dependent simulation optimization.
