Solving a leader–follower facility problem via parallel evolutionary approaches

A leader–follower facility problem is considered in this paper. The objective is to maximize the profit obtained by a chain (the leader) knowing that a competitor (the follower) will react by locating another single facility after the leader locates its own facility. A subpopulation-based evolutiona...

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Veröffentlicht in:	The Journal of supercomputing Jg. 70; H. 2; S. 600 - 611
Hauptverfasser:	Arrondo, A. G., Redondo, J. L., Fernández, J., Ortigosa, P. M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.11.2014
Schlagworte:	Algorithms Chains Compilers Computation Computer Science Evolutionary Interpreters Mathematical models Optimization Parallel processing Processor Architectures Programming Programming Languages Distributed memory Nonlinear bi-level programming problem High performance computing Shared memory Evolutionary algorithm
ISSN:	0920-8542, 1573-0484
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Abstract	A leader–follower facility problem is considered in this paper. The objective is to maximize the profit obtained by a chain (the leader) knowing that a competitor (the follower) will react by locating another single facility after the leader locates its own facility. A subpopulation-based evolutionary algorithm called TLUEGO was recently proposed to cope with this hard-to-solve global optimization problem. However, it requires high computational effort, even to manage small-size problems. In this work, three parallelizations of TLUEGO are proposed, a distributed memory programming algorithm, a shared memory programming algorithm, and a hybrid of the two previous algorithms, which not only allow us to obtain the solution faster, but also to solve larger instances.
AbstractList	A leader–follower facility problem is considered in this paper. The objective is to maximize the profit obtained by a chain (the leader) knowing that a competitor (the follower) will react by locating another single facility after the leader locates its own facility. A subpopulation-based evolutionary algorithm called TLUEGO was recently proposed to cope with this hard-to-solve global optimization problem. However, it requires high computational effort, even to manage small-size problems. In this work, three parallelizations of TLUEGO are proposed, a distributed memory programming algorithm, a shared memory programming algorithm, and a hybrid of the two previous algorithms, which not only allow us to obtain the solution faster, but also to solve larger instances.
Author	Fernández, J. Arrondo, A. G. Ortigosa, P. M. Redondo, J. L.
Author_xml	– sequence: 1 givenname: A. G. surname: Arrondo fullname: Arrondo, A. G. email: agarrondo@um.es organization: Department of Statistics and Operations Research, University of Murcia – sequence: 2 givenname: J. L. surname: Redondo fullname: Redondo, J. L. organization: Department of Computer Architecture and Technology, University of Granada – sequence: 3 givenname: J. surname: Fernández fullname: Fernández, J. organization: Department of Statistics and Operations Research, University of Murcia – sequence: 4 givenname: P. M. surname: Ortigosa fullname: Ortigosa, P. M. organization: Department of Informatics, University of Almería, ceiA3
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Cites_doi	10.1023/A:1011367930251 10.1016/j.omega.2011.02.007 10.1016/S0305-0548(99)00041-6 10.1016/0377-2217(83)90180-7 10.1007/s10898-012-9893-4 10.1023/A:1011224107143 10.1007/978-1-4612-5355-6 10.1016/S0377-2217(00)00169-7 10.1016/S0377-2217(96)00216-0
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Keywords	Distributed memory Nonlinear bi-level programming problem High performance computing Shared memory Evolutionary algorithm
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References	DreznerZHamacherHWFacility location: applications and theory2002BerlinSpringer PlastriaFStatic competitive facility location: an overview of optimisation approachesEur J Oper Res20011293461 OrtigosaPGarcíaIJelásityMReliability and performance of UEGO, a clustering-based global optimizerJ Glob Optim2001193265 Cantú-Paz E (1997) A survey of applications and methods. In: Tech. Rep. IlliGAL 97003, University of Illinois at Urbana-Champaign EiseltHLaporteGSequential location problemsEur J Oper Res1996962217 RedondoJFernándezJArrondoAGarcíaIOrtigosaPA two-level evolutionary algorithm for solving the facility location and design (1\|1)-centroid problem on the plane with variable demandJ Glob Optim2013563983 KilkennyMThisseJEconomics of location: a selective surveyComput Oper Res199926141369 Redondo J, Fernández J, Arrondo A, García I, Ortigosa P (2012) Fixed or variable demand? Does it matter when locating a facility? Omega 40(1):9. doi:10.1016/j.omega.2011.02.007 Francis R, Lowe T, Tamir A (2002) Facility location: application and theory. In: Demand point aggregation for location models, Springer, pp 207–232 HakimiSOn locating new facilities in a competitive environmentEur J Oper Res198312129 JelásityMOrtigosaPGarcíaIUEGO, an abstract clustering technique for multimodal global optimizationJ Heuristics200173215 Drezner Z (ed) (1995) Facility location: a survey of applications and methods. Springer, Berlin 1106_CR1 1106_CR3 1106_CR4 1106_CR5 1106_CR6 1106_CR11 1106_CR7 1106_CR12 1106_CR8 (1106_CR2) 2002 1106_CR9 1106_CR10
References_xml	– reference: Redondo J, Fernández J, Arrondo A, García I, Ortigosa P (2012) Fixed or variable demand? Does it matter when locating a facility? Omega 40(1):9. doi:10.1016/j.omega.2011.02.007 – reference: HakimiSOn locating new facilities in a competitive environmentEur J Oper Res198312129 – reference: Francis R, Lowe T, Tamir A (2002) Facility location: application and theory. In: Demand point aggregation for location models, Springer, pp 207–232 – reference: JelásityMOrtigosaPGarcíaIUEGO, an abstract clustering technique for multimodal global optimizationJ Heuristics200173215 – reference: RedondoJFernándezJArrondoAGarcíaIOrtigosaPA two-level evolutionary algorithm for solving the facility location and design (1\|1)-centroid problem on the plane with variable demandJ Glob Optim2013563983 – reference: EiseltHLaporteGSequential location problemsEur J Oper Res1996962217 – reference: KilkennyMThisseJEconomics of location: a selective surveyComput Oper Res199926141369 – reference: PlastriaFStatic competitive facility location: an overview of optimisation approachesEur J Oper Res20011293461 – reference: Drezner Z (ed) (1995) Facility location: a survey of applications and methods. Springer, Berlin – reference: Cantú-Paz E (1997) A survey of applications and methods. In: Tech. Rep. IlliGAL 97003, University of Illinois at Urbana-Champaign – reference: DreznerZHamacherHWFacility location: applications and theory2002BerlinSpringer – reference: OrtigosaPGarcíaIJelásityMReliability and performance of UEGO, a clustering-based global optimizerJ Glob Optim2001193265 – ident: 1106_CR9 doi: 10.1023/A:1011367930251 – ident: 1106_CR6 doi: 10.1016/j.omega.2011.02.007 – ident: 1106_CR12 – ident: 1106_CR4 doi: 10.1016/S0305-0548(99)00041-6 – ident: 1106_CR8 doi: 10.1016/0377-2217(83)90180-7 – ident: 1106_CR10 – ident: 1106_CR7 doi: 10.1007/s10898-012-9893-4 – ident: 1106_CR11 doi: 10.1023/A:1011224107143 – ident: 1106_CR1 doi: 10.1007/978-1-4612-5355-6 – ident: 1106_CR5 doi: 10.1016/S0377-2217(00)00169-7 – ident: 1106_CR3 doi: 10.1016/S0377-2217(96)00216-0 – volume-title: Facility location: applications and theory year: 2002 ident: 1106_CR2
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Snippet	A leader–follower facility problem is considered in this paper. The objective is to maximize the profit obtained by a chain (the leader) knowing that a... A leader-follower facility problem is considered in this paper. The objective is to maximize the profit obtained by a chain (the leader) knowing that a...
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SubjectTerms	Algorithms Chains Compilers Computation Computer Science Evolutionary Interpreters Mathematical models Optimization Parallel processing Processor Architectures Programming Programming Languages
Title	Solving a leader–follower facility problem via parallel evolutionary approaches
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