Robust intermodal hub location under polyhedral demand uncertainty

• We incorporate demand uncertainty into the hub location problem within a robust optimization framework.
• We propose two Benders decomposition algorithms and compare them.
• We observe that it is possible to obtain significant cost savings with minor changes in the hub locations.

In this study, we consider the robust uncapacitated multiple allocation p-hub median problem under polyhedral demand uncertainty. We model the demand uncertainty in two different ways. The hose model assumes that the only available information is the upper limit on the total flow adjacent at each node, while the hybrid model additionally imposes lower and upper bounds on each pairwise demand. We propose linear mixed integer programming formulations using a minmax criteria and devise two Benders decomposition based exact solution algorithms in order to solve large-scale problems. We report the results of our computational experiments on the effect of incorporating uncertainty and on the performance of our exact approaches.