000108445 001__ 108445
000108445 005__ 20230519145518.0
000108445 0247_ $$2doi$$a10.1016/j.cor.2020.105124
000108445 0248_ $$2sideral$$a122239
000108445 037__ $$aART-2021-122239
000108445 041__ $$aeng
000108445 100__ $$aBlanco, V.
000108445 245__ $$aOn the multisource hyperplanes location problem to fitting set of points
000108445 260__ $$c2021
000108445 5060_ $$aAccess copy available to the general public$$fUnrestricted
000108445 5203_ $$aIn this paper we study the problem of locating a given number of hyperplanes minimizing an objective function of the closest distances from a set of points. We propose a general framework for the problem in which norm-based distances between points and hyperplanes are aggregated by means of ordered median functions. A compact Mixed Integer Linear (or Non Linear) programming formulation is presented for the problem and also an extended set partitioning formulation with a huge number of variables is derived. We develop a column generation procedure embedded within a branch-and-price algorithm for solving the problem by adequately performing its preprocessing, pricing and branching. We also analyze geometrically the optimal solutions of the problem, deriving properties which are exploited to generate initial solutions for the proposed algorithms. Finally, the results of an extensive computational experience are reported. The issue of scalability is also addressed showing theoretical upper bounds on the errors assumed by replacing the original datasets by aggregated versions.
000108445 536__ $$9info:eu-repo/grantAgreement/ES/FEDER/CEI-3-FQM331$$9info:eu-repo/grantAgreement/ES/FEDER/US-1256951$$9info:eu-repo/grantAgreement/ES/MEC-FEDER/MTM2016-74983-C02-01$$9info:eu-repo/grantAgreement/ES/MEC-FEDER/MTM2016-74983-C02-02
000108445 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000108445 590__ $$a5.159$$b2021
000108445 592__ $$a1.855$$b2021
000108445 594__ $$a8.3$$b2021
000108445 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b20 / 87 = 0.23$$c2021$$dQ1$$eT1
000108445 593__ $$aManagement Science and Operations Research$$c2021$$dQ1
000108445 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b35 / 112 = 0.312$$c2021$$dQ2$$eT1
000108445 593__ $$aComputer Science (miscellaneous)$$c2021$$dQ1
000108445 591__ $$aENGINEERING, INDUSTRIAL$$b19 / 50 = 0.38$$c2021$$dQ2$$eT2
000108445 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000108445 700__ $$aJapón, A.
000108445 700__ $$aPonce, D.
000108445 700__ $$aPuerto, J.
000108445 773__ $$g128 (2021), 105124 [15 pp]$$pComput. oper. res.$$tComputers and Operations Research$$x0305-0548
000108445 8564_ $$s514329$$uhttps://zaguan.unizar.es/record/108445/files/texto_completo.pdf$$yPostprint
000108445 8564_ $$s926986$$uhttps://zaguan.unizar.es/record/108445/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000108445 909CO $$ooai:zaguan.unizar.es:108445$$particulos$$pdriver
000108445 951__ $$a2023-05-18-15:19:59
000108445 980__ $$aARTICLE