  We introduce a variant of Multicut Decomposition Algorithms (MuDA), called
CuSMuDA (Cut Selection for Multicut Decomposition Algorithms), for solving
multistage stochastic linear programs that incorporates strategies to select
the most relevant cuts of the approximate recourse functions. We prove the
convergence of the method in a finite number of iterations and use it to solve
six portfolio problems with direct transaction costs under return uncertainty
and six inventory management problems under demand uncertainty. On all problem
instances CuSMuDA is much quicker than MuDA: between 5.1 and 12.6 times quicker
for the porfolio problems considered and between 6.4 and 15.7 times quicker for
the inventory problems.
