  Decision making needs to take an uncertain environment into account. Over the
last decades, robust optimization has emerged as a preeminent method to produce
solutions that are immunized against uncertainty. The main focus in robust
discrete optimization has been on the analysis and solution of one- or
two-stage problems, where the decision maker has limited options in reacting to
additional knowledge gained after parts of the solution have been fixed. Due to
its computational difficulty, multistage problems beyond two stages have
received less attention.
  In this paper we argue that multistage robust discrete problems can be seen
through the lens of quantified integer programs, where powerful tools to reduce
the search tree size have been developed. By formulating both integer and
quantified integer programming formulations, it is possible to compare the
performance of state-of-the-art solvers from both worlds. Using selection,
assignment, lot-sizing and knapsack problems as a testbed, we show that
problems with up to nine stages can be solved to optimality in reasonable time.
