Lagrangean Relaxation has been successfully applied to process many well known\ud instances of NP-hard Mixed Integer Programming problems. In this paper we present\ud a Lagrangean Relaxation based generic solver for processing Mixed Integer\ud Programming problems. We choose the constraints, which are relaxed using a\ud constraint classification scheme. The tactical issue of updating the Lagrange\ud multiplier is addressed through sub-gradient optimisation; alternative rules for\ud updating their values are investigated. The Lagrangean relaxation provides a lower\ud bound to the original problem and the upper bound is calculated using a heuristic\ud technique. The bounds obtained by the Lagrangean Relaxation based generic solver\ud were used to warm-start the Branch and Bound algorithm; the performance of the\ud generic solver and the effect of the alternative control settings are reported for a wide\ud class of benchmark models. Finally, we present an alternative technique to calculate\ud the upper bound, using a genetic algorithm that benefits from the mathematical\ud structure of the constraints. The performance of the genetic algorithm is also\ud presented
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.