  Multistage stochastic programming is a powerful tool allowing decision-makers
to revise their decisions at each stage based on the realized uncertainty.
However, in practice, organizations are not able to be fully flexible, as
decisions cannot be revised too frequently due to their high organizational
impact. Consequently, decision commitment becomes crucial to ensure that
initially made decisions remain unchanged for a certain period. This paper
introduces adaptive multistage stochastic programming, a new optimization
paradigm that strikes an optimal balance between decision flexibility and
commitment by determining the best stages to revise decisions depending on the
allowed level of flexibility. We introduce a novel mathematical formulation and
theoretical properties eliminating certain constraint sets. Furthermore, we
develop a decomposition method that effectively handles mixed-integer adaptive
multistage programs by adapting the integer L-shaped method and Benders
decomposition. Computational experiments on stochastic lot-sizing and
generation expansion planning problems show substantial advantages attained
through optimal selections of revision times when flexibility is limited, while
demonstrating computational efficiency of the proposed properties and solution
methodology. Optimizing revision times in a less flexible case can outperform
arbitrary selection in a more flexible case. By adhering to these optimal
revision times, organizations can achieve performance levels comparable to
fully flexible settings.
