Solving the simple plant location problem using a data correcting approach

Abstract

The Data Correcting Algorithm is a branch and bound algorithm in which thedata of a given problem instance is ‘corrected’ at each branching in such a waythat the new instance will be as close as possible to a polynomially solvableinstance and the result satisfies an acceptable accuracy (the difference betweenoptimal and current solution). In this paper the data correcting algorithm isapplied to determining exact and approximate optimal solutions to the simpleplant location problem. Implementations of the algorithm are based on apseudo-Boolean representation of the goal function of the SPLP and a newreduction rule. We study the efficiency of the data correcting approach usingtwo different bounds, the combinatorial bound and the Erlenkotter bound. Wepresent computational results on several benchmark instances of the simpleplant location problem, which confirm the efficiency of the data-correcting approach.