Abstract
In this paper we introduce an optimization problem, that arises in the competitive facility location area, which involves the maximization of the weighted area of the region where a new facility has influence. We consider a finite set of points S in a bounded polygonal region domain D subdivided into several non-negative weighted regions according to a weighted domain partition \(\mathcal{P}\). For each point in S we define its k-nearest/farthest neighbor influence region as the region containing all the points of D having the considered point as one of their k-nearest/farthest neighbors in S. We want to find a new point s in D whose k-influence region is maximal in terms of weighted area according to the weighted partition \(\mathcal{P}\). We present a GPU parallel approach, designed under CUDA architecture, for approximately solving the problem and we also provide experimental results showing the efficiency and scalability of the approach.
Work partially supported by the Spanish Ministerio de Ciencia e Innovación under grant TIN2010-20590-C02-02.
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Fort, M., Sellarès, J.A. (2012). GPU-Based Influence Regions Optimization. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31125-3_20
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DOI: https://doi.org/10.1007/978-3-642-31125-3_20
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