  We consider the vehicle routing problem with stochastic demands (VRPSD), a
problem in which customer demands are known in distribution at the route
planning stage and revealed during route execution upon arrival at each
customer. A long-standing open question on the VRPSD concerns the benefits of
allowing, during route execution, partial reordering of the planned customer
visits. Given the practical importance of this question and the growing
interest on the VRPSD under optimal restocking, we study the VRPSD under a
recourse policy known as the switch policy. The switch policy is a canonical
reoptimization policy that permits only pairs of successive customers to be
reordered. We consider this policy jointly with optimal preventive restocking
and introduce a branch-cut-and-price algorithm to compute optimal a priori
routing plans. This algorithm features pricing routines where value functions
represent the expected cost-to-go along planned routes for all possible states
and reordering decisions. To ensure pricing tractability, we adopt a strategy
that combines elementary pricing with completion bounds of varying complexity,
and solve the pricing problem without relying on dominance rules. Our numerical
experiments demonstrate the effectiveness of the algorithm for solving
instances with up to 50 customers. Notably, they also give us new insights into
the value of reoptimization. The switch policy enables significant cost savings
over optimal restocking when the planned routes come from an algorithm built on
a deterministic approximation of the data, an important scenario given the
difficulty of finding optimal VRPSD solutions. The benefits are smaller when
comparing optimal a priori VRPSD solutions obtained for both recourse policies.
As it appears, further cost savings may require joint reordering and
reassignment of customer visits among vehicles when the context permits.
