Capacity inverse minimum cost flow problem

Given a directed graph G = (N,A) with arc capacities uij and a minimum cost flow problem defined on G, the capacity inverse minimum cost flow problem is to find a new capacity vector û for the arc set A such that a given feasible flow x̂ is optimal with respect to the modified capacities. Among all capacity vectors û satisfying this condition, we would like to find one with minimum ‖û− u‖ value. We consider two distance measures for ‖û − u‖, rectilinear (L1) and Chebyshev (L∞) 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.