Publicação
Distribution of the number of losses in busy-periods of oscillating Mx/G/1/(n,a,b) systems
| datacite.subject.fos | Ciências Naturais::Matemáticas | |
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| dc.contributor.author | Ferreira, Fátima | |
| dc.contributor.author | Pacheco, António | |
| dc.contributor.author | Ribeiro, Helena | |
| dc.date.accessioned | 2026-03-05T11:23:10Z | |
| dc.date.available | 2026-03-05T11:23:10Z | |
| dc.date.issued | 2021-05 | |
| dc.description.abstract | We analyze customer losses in busy-periods of preemptive and non-preemptive oscillating MX/G/1/(n,a,b) systems. Specifically, we propose a recursive scheme to compute the probability function of the number of customer losses in busy-periods of such systems. The effectiveness and usefulness of the proposed method is illustrated through the presentation of numerical examples that consider different arrival rates and service time and group size distributions. | eng |
| dc.description.sponsorship | This research was partially supported by Fundação para a Ciência e a Tecnologia (FCT) through project UID/Multi/04621/2019. | |
| dc.identifier.citation | Fátima Ferreira, António Pacheco, Helena Ribeiro, Distribution of the number of losses in busy-periods of oscillating MX/G/1/(n,a,b) systems, Computers & Operations Research, Volume 129, 2021, 105180, ISSN 0305-0548, https://doi.org/10.1016/j.cor.2020.105180. | |
| dc.identifier.doi | 10.1016/j.cor.2020.105180 | |
| dc.identifier.eissn | 1873-765X | |
| dc.identifier.issn | 0305-0548 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/15785 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier | |
| dc.relation | Center for Computational and Stochastic Mathematics | |
| dc.relation.hasversion | https://www.sciencedirect.com/science/article/pii/S0305054820302975?via%3Dihub | |
| dc.relation.ispartof | Computers & Operations Research | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Busy-period | |
| dc.subject | Customer losses | |
| dc.subject | Markov regenerative processes | |
| dc.subject | Non-preemptive systems | |
| dc.subject | Oscillating queues | |
| dc.subject | Preemptive systems | |
| dc.title | Distribution of the number of losses in busy-periods of oscillating Mx/G/1/(n,a,b) systems | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UID/Multi/04621/2019 | |
| oaire.awardTitle | Center for Computational and Stochastic Mathematics | |
| oaire.awardURI | http://hdl.handle.net/10400.8/15783 | |
| oaire.citation.endPage | 11 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Computers and Operations Research | |
| oaire.citation.volume | 129 | |
| oaire.fundingStream | Financiamento do Plano Estratégico de Unidades de I&D - 2019 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Ribeiro | |
| person.givenName | Helena | |
| person.identifier | 1570599 | |
| person.identifier.ciencia-id | 161F-B79B-D660 | |
| person.identifier.orcid | 0000-0002-2429-7991 | |
| person.identifier.rid | E-2944-2015 | |
| person.identifier.scopus-author-id | 35896704400 | |
| relation.isAuthorOfPublication | 51734878-db02-4a67-906d-a0161ed2e2dc | |
| relation.isAuthorOfPublication.latestForDiscovery | 51734878-db02-4a67-906d-a0161ed2e2dc | |
| relation.isProjectOfPublication | b4e5c81e-491a-4e55-9b95-bb3f20270698 | |
| relation.isProjectOfPublication.latestForDiscovery | b4e5c81e-491a-4e55-9b95-bb3f20270698 |
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- We analyze customer losses in busy-periods of preemptive and non-preemptive oscillating MX/G/1/(n,a,b) systems. Specifically, we propose a recursive scheme to compute the probability function of the number of customer losses in busy-periods of such systems. The effectiveness and usefulness of the proposed method is illustrated through the presentation of numerical examples that consider different arrival rates and service time and group size distributions.
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