  To integrate strategic, tactical and operational decisions, the two-stage
optimization has been widely used to guide dynamic decision making. In this
paper, we study the two-stage stochastic programming for complex systems with
unknown response estimated by simulation. We introduce the global-local
metamodel assisted two-stage optimization via simulation that can efficiently
employ the simulation resource to iteratively solve for the optimal first- and
second-stage decisions. Specifically, at each visited first-stage decision, we
develop a local metamodel to simultaneously solve a set of scenario-based
second-stage optimization problems, which also allows us to estimate the
optimality gap. Then, we construct a global metamodel accounting for the errors
induced by: (1) using a finite number of scenarios to approximate the expected
future cost occurring in the planning horizon, (2) second-stage optimality gap,
and (3) finite visited first-stage decisions. Assisted by the global-local
metamodel, we propose a new simulation optimization approach that can
efficiently and iteratively search for the optimal first- and second-stage
decisions. Our framework can guarantee the convergence of optimal solution for
the discrete two-stage optimization with unknown objective, and the empirical
study indicates that it achieves substantial efficiency and accuracy.
