An iterative algorithm for the multifacility minimax location problem with euclidean distances

An iterative solution method is presented for solving the multifacility location problem with Euclidean distances under the minimax criterion. The iterative procedure is based on the transformation of the multifacility minimax problem into a sequence of squared Euclidean minisum problems which have analytical solutions. Computational experience with the new method is also presented.     

Solving multifacility location problems involving euclidean distances

This paper considers the problem of locating multiple new facilities in order to minimize a total cost function consisting of the sum of weighted Euclidean distances among the new facilities and between the new and existing facilities, the locations of which are known. A new procedure is derived from a set of results pertaining to necessary conditions for a minimum of the objective function. The results from a number of sample problems which have been executed on a programmed version of this algorithm are used to illustrate the effectiveness of the new technique.     

Acceleration of the HAP approach for the multifacility location problem

This article considers the problem of locating multiple new facilities to minimize the cost function consisting of the sum of weighted distances among new facilities and between new and existing facilities. The hyperboloid approximate procedure (HAP) is probably the most widely used approach for solving this problem. In this article, an optimality condition for this problem is derived and a method to accelerate the convergence rate of the HAP for the case of Euclidean distances is presented. From the numerical results presented in this article, it can be concluded that the performance of the new algorithm is superior to the performance of the original HAP.