As one of the most important models in stochastic problem, the chance constrained optimization problem has been widely used in the fields of finance, engineering, management and so on. As the practical problems become more and more complex, the probability distribution of the uncertainty is difficult to predict/estimate accurately. Distributionally robust chance constrained optimization problem, as an effective model with ambiguous distributional information about uncertainty, has been proposed in the literature. In recent years, researchers have constantly developed new models for distributionally robust chance constrained optimization problems. The main purpose of this paper is to review recent advances in emerging models for distributionally robust chance constrained optimization problems and their potential applications in practice.
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