The robust shortest path problem with interval data uncertainties

In this study, we investigate the well-known shortest path problem on directed acyclic graphs under arc length uncertainties. We structure data uncertainty by taking the arc lengths as interval ranges. In order to handle uncertainty in the decision making process, we believe that a robustness approach is appropriate to use. The robustness criteria we used are the minimax (absolute robustness) criterion and the minimax regret (relative robustness) criterion. Under these criteria, we de ne and identify paths which perform satisfactorily under any likely input data and give mixed integer programming formulation to nd them. In order to simplify decision making, we classify arcs based on the realization of the input data. We show that knowing which arcs are always on shortest paths and which arcs are never on shortest paths we can preprocess a graph for robust path problems. Computational results support our claim that the preprocessing of graphs helps us signi cantly in solving the robust path problems.