The reduced cost branch and bound algorithm for mixed integer programming

Abstract

A different branch and bound algorithm for mixed integer programming is presented. Unlike standard linear programming based branch and bound algorithms, where a single fractional variable (or Special Ordered Set) is selected for problem separation, the proposed method selects groups of variables for separation on the basis of their reduced cost in an LP relaxation. The proposed method restricts a large portion of the integer variables to zero on one branch. The net effect is that the original integer program is solved by optimizing a series of smaller, more tightly restricted, integer programs. The authors have programmed the algorithm using the Extended Control Language of the IBM MPSX/370-MIP/370 mixed integer programming package. Computational results are presented that demonstrate the efficiency of the method on problems where the 0 1 variables are partitioned into multiple choice constraints containing special ordered sets of variables. While the computational results are limited to this class of problems the algorithm can, in theory, be applied to any mixed integer programming problem.