  We propose a methodology at the nexus of operations research and machine
learning (ML) leveraging generic approximators available from ML to accelerate
the solution of mixed-integer linear two-stage stochastic programs. We aim at
solving problems where the second stage is highly demanding. Our core idea is
to gain large reductions in online solution time while incurring small
reductions in first-stage solution accuracy by substituting the exact
second-stage solutions with fast, yet accurate supervised ML predictions. This
upfront investment in ML would be justified when similar problems are solved
repeatedly over time, for example, in transport planning related to fleet
management, routing and container yard management.
  Our numerical results focus on the problem class seminally addressed with the
integer and continuous L-shaped cuts. Our extensive empirical analysis is
grounded in standardized families of problems derived from stochastic server
location (SSLP) and stochastic multi knapsack (SMKP) problems available in the
literature. The proposed method can solve the hardest instances of SSLP in less
than 9% of the time it takes the state-of-the-art exact method, and in the case
of SMKP the same figure is 20%. Average optimality gaps are in most cases less
than 0.1%.
