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Abstract
Recently, we proposed a d.c. (difference of convex) optimization method for finding global solutions to single facility location problems having general attraction and repulsion functions. The method, which is based on a representation of the nonconvex objective function as the difference of two convex (d.c.) functions, was implemented and results on solving problems with up to 100,000 attractors and repellers were reported. In this paper we extend our method to solve three generalizations of the model: maximin location problems, competitive location problems and constrained location problems. Extensive computational experiment's with an implementation of the procedure are reported.
