Consider a point-to-point transportation network consisting of M nodes. By "point-to-point" we mean that, given transportation requirements between all pair of nodes, there is a direct transportation link from each origin to each destination, without transshipment through intermediate nodes (this should be understood as a simplification of the real-life problem). Each node, in general, is both a source and a destination for many such requirements.

Transportation requires containers of given volume capacity, measured in the same units as transportation requirements. At present, we know how many empty containers are present at each node in the network. Before transportation needs arise, we should consider repositioning containers, so that we will be in a better position to meet all requirements. Repositioning is carried out now, and transportation occurs in the next period. We know the repositioning cost for each container, for each pair of nodes in the network (say that they essentially depend on traveled distance). What we do not know are the future transportation requirements, but we are able to generate a set of plausible scenarios. Each scenario is essentially a matrix describing the transportation requirement for each pair of nodes (the diagonal elements are zero), associated with a probability. If, when requirements are revealed, we do not have enough containers to satisfy them at a node, we have to rent containers, which implies a rather high fixed cost per container. For the sake of simplicity, let us assume that such cost does not depend on nodes, nor traveled distances. On the one hand, we would not like to move too many containers; on the other one, we do not want to spend too much money to rent containers if spikes in transportation demand occur at some nodes. Build a two-stage stochastic programming model to minimize the total expected cost.