Artificial Bee Colony Optimization Algorithm for Uncapacitated Facility Location Problems.
In this paper, a new multiple facility location and allocation method is proposed. This algorithm assigns demand points to pre-determined facilities which have infinite capacities, by using an artificial bee colony optimization with local search to solve discrete uncapacitated multiple facility loca...
| Publicado en: | Journal of Economic & Social Research Vol. 14; no. 1; pp. 1 - 25 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Department of Economics at Fatih University
2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=91512020&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 91512020 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 13021060 KXM jtl: Journal of Economic & Social Research issn: 13021060 maglogo: N pubinfo: dt: 2012 vid: 14 iid: 1 pid: 14222 pub: Department of Economics at Fatih University artinfo: ui: 91512020 ppf: 1 ppct: 24 formats: tig: atl: Artificial Bee Colony Optimization Algorithm for Uncapacitated Facility Location Problems. aug: au: Tuncbilek, Nukhet Tasgetiren, Fatih Esnaf, Sakir affil: Istanbul Kultur University, Istanbul Yasar University, Izmir Istanbul University, Istanbul su: Türkiye Colonies Facility location problems Mathematical optimization Algorithms Particle swarm optimization Transportation costs sug: subj: Colonies Türkiye Facility location problems Mathematical optimization Algorithms Particle swarm optimization Transportation costs keyword: Artificial Bee Colony Optimization Discrete Uncapacitated Facility Location Local Search Artificial Bee Colony Optimization Discrete Uncapacitated Facility Location Local Search ab: In this paper, a new multiple facility location and allocation method is proposed. This algorithm assigns demand points to pre-determined facilities which have infinite capacities, by using an artificial bee colony optimization with local search to solve discrete uncapacitated multiple facility location problems. This method is tested and compared with continuous and discrete particle swarm optimization based facility location methods on alternative models with or without local search for optimizing the well-known benchmark problems and generated problems.Similar test is conducted on actual demand and transportation cost data from a fertilizer manufacturer from Turkey, which includes 11 facility locations and 768 demand points. Proposed ABC-based algorithm exhibited better performance than the PSO-based algorithms. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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