A revised reformulation-linearization technique for the quadratic assignment problem

The Reformulation Linearization Technique (RLT) applied to the Quadratic Assignment Problem yields mixed 0–1 programming problems whose linear relaxations provide a strong bound on the objective value. Nevertheless, in the high level RLT representations the computation requires much effort. In this paper we propose a new compact reformulation for each level of the RLT representation exploiting the structure of the problem. Computational results on some benchmark instances indicate the potential of the new RLT representations as the level of the RLT increases.