Elimination of Source A and B Errors in p-Median Location Problems

The p-median problem is a powerful tool in analyzing facility location options when the goal of the location scheme is to minimize the average distance that demand must traverse to reach its nearest facility. It may be used to determine the number of facilities to site, as well as the actual facility locations. Demand data are frequently aggregated in p-median location problems to reduce the computational complexity of the problem. Demand data aggregation, however, results in the loss of locational information. This loss may lead to suboptimal facility location configurations (optimality errors) and inaccurate measures of the resulting travel distances (cost errors). Hillsman and Rhoda (1978) have identified three error components: Source A, B, and C errors, which may result from demand data aggregation. In this article, a method to measure weighted travel distances in p-median problems which eliminates Source A and B errors is proposed. Test problem results indicate that the proposed measurement scheme yields solutions with lower optimality and cost errors than does the traditional distance measurement scheme.     

Recursive Algorithm for Determination of Proximal (Thiessen) Polygons in Any Metric Space

A new and recursive algorithm for determining proximal polygons is based on quadtree concepts. The algorithm can use any distance metric, and produces a quadtree of the image of the proximal polygons. This can be converted to vector form, or be incorporated directly into a quadtree-based geographic information system, in order to solve a number of closest-point problems. The algorithm can also produce the Delaunay triangulation, which is the dual of the proximal polygons. Empirically, the running time of the algorithm is proportional to the number of centers raised to the 1.6 power.     

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