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A New Compact Formulation for Discrete p-Dispersion

David Sayah () and Stefan Irnich ()
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David Sayah: Johannes Gutenberg University Mainz
Stefan Irnich: Johannes Gutenberg University Mainz

No 1517, Working Papers from Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz

Abstract: This paper addresses the discrete p-dispersion problem (PDP) which is about selecting p facilities from a given set of candidates in such a way that the minimum distance between selected facilities is maximized. We propose a new compact formulation for this problem. In addition, we discuss two simple enhancements of the new formulation: Simple bounds on the optimal distance can be exploited to reduce the size and to increase the tightness of the model at a relatively low cost of additional computation time. Moreover, the new formulation can be further strengthened by adding valid inequalities. We present a computational study carried out over a set of large-scale test instances in order to compare the new formulation against a standard mixed-integer programming model of the PDP, a line search, and a binary search. Our numerical results indicate that the new formulation in combination with the simple bounds is solved to optimality by an out-of-the-box mixed-integer programming solver in 34 out of 40 instances, while this is neither possible with the standard model nor with the search procedures. For instances in which the line and binary search fail to ?nd a provably optimal solution, we achieve this by adding cuts to our enhanced formulation.

Keywords: facility location; dispersion problems; max-min objective; integer programming (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp
Date: 2015-11-05
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https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1517.pdf First version, 2015 (application/pdf)

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