  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 extended to the multi-stage setting. This survey
reviews the models and methodologies of single-stage optimization via
simulation and multi-stage stochastic programming. These are necessary
theoretical components to push forward the development of sequential decision
making via simulation. We identify the key challenge of sequential decision
making via simulation as the appropriate modeling of the stage-wise value
function, for which we survey the state-of-the-art meta-models and their
potential solution algorithms.
