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dc.contributor.authorBasagoiti, Rosa
dc.contributor.otherBeamurgia Bengoa, Maite
dc.contributor.otherRodríguez, I.
dc.contributor.otherRodriguez Chacón, Victoria
dc.date.accessioned2022-09-02T14:21:49Z
dc.date.available2022-09-02T14:21:49Z
dc.date.issued2022
dc.identifier.issn1432-7643en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168077en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5658
dc.description.abstractPassenger waiting time is a significant issue related to the quality of service of a multiple lift system; however, energy consumption reduction is also an important concern in the lift industry. In this paper, we evaluate different versions of a genetic algorithm (GA) published previously by the authors with several relevant adjustments for the lift dispatching problem to minimize passenger waiting time and/or energy consumption. To the raw GA with adjustments (that works under the assumption one call-one passenger) we incorporated several elements: a passenger-counting module using origin-destination (OD) matrices, and the activation of certain policies (zoning and/or parking) under different detected traffic profiles (up-peak, interfloor or down-peak profiles). Besides, we added a proportional integral controller (PI) to assign different weights to passenger waiting time and energy consumption to evaluate the performance of our GA. Different versions of this GA, minimizing passenger waiting time and/or energy consumption, were compared among them and to a conventional control algorithm using three different types of simulated profiles: a mixed one, three well-known full day office profiles, and three different step profiles. The results showed that the bi-objective GA version with the estimation of the number of passengers behind a call, i.e., the passenger forecasting, together with the parking policy for up-peak or down peak conditions significantly improved performance of passenger waiting time, and in some cases in energy consumption as well. The addition of the PI controller to the GA proved to be especially useful when the system was under a high intensity traffic demand. The advantages of all these elements to forecast the passenger flow and detect the traffic profile to help the controller shows unquestionable benefits to minimize passenger waiting time and energy consumption.en
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherSpringeren
dc.rightsThe Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022en
dc.subjectGenetic algorithmen
dc.subjectElevator dispatching problemen
dc.subjectPassenger Flow Patternsen
dc.subjectTransportationen
dc.titleImproving Waiting Time and Energy Consumption Performance of a Bi- objective Genetic Algorithm embedded in an Elevator Group Control System through passenger flow estimationen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceSoft Computingen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1007/s00500-022-07358-4en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2022-2025/IT1676-22/CAPV/Sistemas Inteligentes para Sistemas Industriales/en
local.embargo.enddate2023-08-31
local.contributor.otherinstitutionFagor AOTEKes
local.contributor.otherinstitutionhttps://ror.org/02rxc7m23es
local.source.details2022. First online 07 August 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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