We propose a novel approach using supervised learning to obtain near-optimal
primal solutions for two-stage stochastic integer programming (2SIP) problems
with constraints in the first and second stages. The goal of the algorithm is
to predict a "representative scenario" (RS) for the problem such that,
deterministically solving the 2SIP with the random realization equal to the RS,
gives a near-optimal solution to the original 2SIP. Predicting an RS, instead
of directly predicting a solution ensures first-stage feasibility of the
solution. If the problem is known to have complete recourse, second-stage
feasibility is also guaranteed. For computational testing, we learn to find an
RS for a two-stage stochastic facility location problem with integer variables
and linear constraints in both stages and consistently provide near-optimal
solutions. Our computing times are very competitive with those of
general-purpose integer programming solvers to achieve a similar solution
quality