| People | Locations | Statistics |
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| Mouftah, Hussein T. |
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| Dugay, Fabrice |
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| Rettenmeier, Max |
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| Tomasch, Ernst | Graz |
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| Cornaggia, Greta |
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| Palacios-Navarro, Guillermo |
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| Uspenskyi, Borys V. |
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| Khan, Baseem |
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| Fediai, Natalia |
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| Derakhshan, Shadi |
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| Somers, Bart | Eindhoven |
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| Anvari, B. |
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| Kraushaar, Sabine | Vienna |
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| Kehlbacher, Ariane |
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| Das, Raj |
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| Werbińska-Wojciechowska, Sylwia |
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| Brillinger, Markus |
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| Eskandari, Aref |
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| Gulliver, J. |
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| Loft, Shayne |
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| Kud, Bartosz |
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| Matijošius, Jonas | Vilnius |
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| Piontek, Dennis |
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| Kene, Raymond O. |
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| Barbosa, Juliana |
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Pelegrín, Blas
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2023On the Existence and Computation of Nash Equilibrium in Network Competitive Location Under Delivered Pricing and Price Sensitive Demandcitations
- 2018Computation of Multi-facility Location Nash Equilibria on a Network Under Quantity Competitioncitations
- 2016Profit maximization and reduction of the cannibalization effect in chain expansioncitations
- 2012Isodistant points in competitive network facility locationcitations
- 2011Location strategy for a firm under competitive delivered pricescitations
- 2008A practical algorithm for decomposing polygonal domains into convex polygons by diagonalscitations
- 2007Planar Location and Design of a New Facility with Inner and Outer Competition: An Interval Lexicographical-like Solution Procedurecitations
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document
Isodistant points in competitive network facility location
Abstract
An isodistant point is any point on a network which is located at a predetermined distance from some node. For some competitive facility location problems on a network, it is verified that optimal (or near-optimal) locations are found in the set of nodes and isodistant points (or points in the vicinity of isodistant points). While the nodes are known, the isodistant points have to be determined for each problem. Surprisingly, no algorithm has been proposed to generate the isodistant points on a network. In this paper, we present a variety of such problems and propose an algorithm to find all isodistant points for given threshold distances associated with the nodes. The number of isodistant points is upper bounded by nm , where n and m are the number of nodes and the number of edges, respectively. Computational experiments are presented which show that isodistant points can be generated in short run time and the number of such points is much smaller than nm . Thus, for networks of moderate size, it is possible to find optimal (or near-optimal) solutions through the Integer Linear Programming formulations corresponding to the discrete version of such problems, in which a finite set of points are taken as location candidates.
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