Robust Quadratic Assignment Problem with Uncertain Locations

 We consider a generalization of the classical quadratic assignment problem, where coordinates of locations are uncertain and only upper and lower bounds are known for each coordinate. We develop a mixed integer linear programming model as a robust counterpart of the proposed uncertain model. A key challenge is that, since the uncertain model involves nonlinear objective function of the uncertain data, classical robust optimization approaches cannot be applied directly to construct its robust counterpart. We exploit the problem structure to develop exact solution methods and present some computational results.