  We report a computational study of cutting plane algorithms for multi-stage
stochastic mixed-integer programming models with the following cuts: (i)
Benders', (ii) Integer L-shaped, and (iii) Lagrangian cuts. We first show that
Integer L-shaped cuts correspond to one of the optimal solutions of the
Lagrangian dual problem, and, therefore, belong to the class of Lagrangian
cuts. To efficiently generate these cuts, we present an enhancement strategy to
reduce time-consuming exact evaluations of integer subproblems by alternating
between cuts derived from the relaxed and exact computation. Exact evaluations
are only employed when Benders' cut from the relaxation fails to cut off the
incumbent solution. Our preliminary computational results show the merit of
this approach on multiple classes of real-world problems.
