  This paper generalized the theory of scenario programming (SP) deeming that,
as a direct data-driven optimization paradigm, the SP is an ideal solution to
decision-making under uncertainty in the era of data. First, a general SP (GSP)
problem under both exogenous and endogenous uncertainty is defined and its
mathematical formulation in the form of nonlinear program is developed. Then,
some key aspects of the GSP theory are discussed, which include its relation
with the existing logic models of optimization under uncertainty, a confidence
theorem under random sampling, and a new direction of high-performance solution
methods based on strategic sampling. Finally, the proposed methods are
preliminarily applied to a smartgrid optimization problem under endogenous
uncertainty.
