Compact MILP formulations for the $p$-center problem

Autor: Sourour Elloumi, Zacharie Alès
Přispěvatelé: Unité de Mathématiques Appliquées ( UMA ), École Nationale Supérieure de Techniques Avancées ( Univ. Paris-Saclay, ENSTA ParisTech ), Centre d'Etude et De Recherche en Informatique du Cnam ( CEDRIC ), Conservatoire National des Arts et Métiers [CNAM] ( CNAM ), CEDRIC. Optimisation Combinatoire (CEDRIC - OC), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Optimisation et commande (OC), Unité de Mathématiques Appliquées (UMA), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Jon Lee, Giovanni Rinaldi, A. Ridha Mahjoub, Ales, Zacharie, École Nationale Supérieure de Techniques Avancées (ENSTA Paris)
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: International Symposium on Combinatorial Optimization ISCO 2018
International Symposium on Combinatorial Optimization ISCO 2018, Apr 2018, Marrakech, Morocco. 2018
Combinatorial Optimization
Jon Lee; Giovanni Rinaldi; A. Ridha Mahjoub. Combinatorial Optimization, 10856, Springer, pp.14-25, 2018, Lecture Notes in Computer Science, 978-3-319-96151-4. ⟨10.1007/978-3-319-96151-4_2⟩
ISCO (International Symposium on Combinatorial Optimization) 2018
ISCO (International Symposium on Combinatorial Optimization) 2018, Apr 2018, Marrakesh, France
Lecture Notes in Computer Science ISBN: 9783319961507
ISCO
DOI: 10.1007/978-3-319-96151-4_2⟩
Popis: The p-center problem consists in selecting p centers among M to cover N clients, such that the maximal distance between a client and its closest selected center is minimized. For this problem we propose two new and compact integer formulations. Our first formulation is an improvement of a previous formulation. It significantly decreases the number of constraints while preserving the optimal value of the linear relaxation. Our second formulation contains less variables and constraints but it has a weaker linear relaxation bound. We besides introduce an algorithm which enables us to compute strong bounds and significantly reduce the size of our formulations. Finally, the efficiency of the algorithm and the proposed formulations are compared in terms of quality of the linear relaxation and computation time over instances from OR-Library.
Lecture Notes in Computer Science 2018
Databáze: OpenAIRE