  Chance-constrained programming (CCP) is one of the most difficult classes of
optimization problems that has attracted the attention of researchers since the
1950s. In this survey, we first review recent developments in mixed-integer
linear formulations of chance-constrained programs that arise from finite
discrete distributions (or sample average approximation). We highlight
successful reformulations and decomposition techniques that enable the solution
of large-scale instances. We then review active research in distributionally
robust CCP, which is a framework to address the ambiguity in the distribution
of the random data. The focal point of our review is scalable formulations that
can be readily implemented with state-of-the-art optimization software.
However, we also discuss alternative approaches and specialized algorithms.
Furthermore, we highlight the prevalence of CCPs with a review of applications
across multiple domains.
