Given a directed graph G = (N,A) with arc capacities u and a minimum cost flow problem defined on G, the capacity inverse minimum cost flow problem is to find a new capacity vector u' for the arc set A such that a given feasible flow x' is optimal with respect to the modified capacities. Among all capacity vectors u' satisfying this condition, we would like to find one with minimum ||u' - u|| value. We consider two distance measures for ||u' - u||, rectilinear and Chebyshev distances. By reduction from the feedback arc set problem we show that the capacity inverse minimum cost flow problem is NP-hard in the rectilinear case. On the other hand, it is polynomially solvable by a greedy algorithm for the Chebyshev norm. In the latter case we propose a heuristic for the bicriteria problem, where we minimize among all optimal solutions the number of affected arcs. We also present computational results for this heuristic.
In contrast to p-hub problems with a summation objective (p-hub median), minmax hub problems (p-hub center) have not attained much attention in the literature. In this paper, we give a polyhedral analysis of the uncapacitated single allocation p-hub center problem (USApHCP). The analysis will be based on a radius formulation which currently yields the most efficient solution procedures. We show which of the valid inequalities in this formulation are facet-defining and present non-elementary classes of facets, for which we propose separation problems. A major part in our argumentation will be the close connection between polytopes of the USApHCP and the uncapacitated p-facility location (pUFL). Hence, the new classes of facets can also be used to improve pUFL formulations.