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José Miguel Díaz-Báñez

Researcher at University of Seville

Publications -  122
Citations -  1153

José Miguel Díaz-Báñez is an academic researcher from University of Seville. The author has contributed to research in topics: Facility location problem & Robot. The author has an hindex of 17, co-authored 114 publications receiving 1057 citations.

Papers
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Journal ArticleDOI

Continuous location of dimensional structures

TL;DR: A review of the existing work on the location of extensive facilities in continuous spaces is given and gaps in the knowledge are identified and suggestions for further research are made.
Journal ArticleDOI

One-to-One Coordination Algorithm for Decentralized Area Partition in Surveillance Missions with a Team of Aerial Robots

TL;DR: A decentralized algorithm for area partition in surveillance missions that ensures information propagation among all the robots in the team that fulfils all the goals and requirements and has been also compared to other strategies.
Proceedings Article

Reverse facility location problems

TL;DR: In this paper, the Bichromatic Reverse Nearest Neighbor (BRNN) problem was introduced and investigated, and algorithms were proposed to compute a new blue point such that: (1) the number of associated red points is maximum (MAXCOV criterion); (2) the maximum distance to the associated RED points is minimum (MINMAX criterion); and (3) the minimum distance to this paper.

Reverse facility location problems

TL;DR: This paper introduces and investigates optimization problems according to the Bichromatic Reverse Nearest Neighbor rule (BRNN), and gives efficient algorithms to compute a new blue point such that the number of associated red points is maximum (MAXCOV criterion) and the maximum distance to the associatedred points is minimum (MINMAX criterion).
Journal ArticleDOI

Facility location problems in the plane based on reverse nearest neighbor queries

TL;DR: This paper introduces and study new optimization problems in the plane based on the bichromatic reverse nearest neighbor (BRNN) rule and provides efficient algorithms to compute a new blue point under criteria such as: the number of associated red points is maximum (MAXCOV criterion); the maximum distance to the associated red Points is minimum (MINMAX criterion).