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dc.contributor.authorArrieta, Aitor
dc.contributor.authorSagardui, Goiuria
dc.contributor.otherGalarraga Arotzena, Joritz
dc.contributor.otherAli, Shaukat
dc.contributor.otherArratibel, Maite
dc.date.accessioned2022-07-06T14:46:04Z
dc.date.available2022-07-06T14:46:04Z
dc.date.issued2021
dc.identifier.isbn978-1-6654-2603-9en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167703en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5631
dc.description.abstractElevators, as other cyber-physical systems, need to deal with uncertainty during their operation due to several factors such as passengers and hardware. Such uncertainties could affect the quality of service promised by elevators and in the worst case lead to safety hazards. Thus, it is important that elevators are extensively tested by considering uncertainty during their development to ensure their safety in operation. To this end, we present an uncertainty testing methodology supported with a tool to test industrial dispatching systems at the Software-in-the-Loop (SiL) test level. In particular, we focus on uncertainties in passenger data and employ a Genetic Algorithm (GA) with specifically designed genetic operators to significantly reduce the quality of service of elevators, thus aiming to find uncertain situations that are difficult to extract by users. An initial experiment with an industrial dispatcher revealed that the GA significantly decreased the quality of service as compared to not considering uncertainties. The results can be used to further improve the implementation of dispatching algorithms to handle various uncertainties.en
dc.description.sponsorshipComisión Europeaes
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectElevatorsen
dc.subjectgenetic algorithmsen
dc.subjectQuality of Serviceen
dc.subjectuncertaintyen
dc.subjectpassenger dataen
dc.subjectsoftware in the loop simulationen
dc.titleGenetic Algorithm-based Testing of Industrial Elevators under Passenger Uncertaintyen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.source2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)en
local.contributor.groupIngeniería del software y sistemases
local.description.peerreviewedtrueen
local.description.publicationfirstpage353en
local.description.publicationlastpage358en
local.identifier.doihttps://doi.org/10.1109/ISSREW53611.2021.00101en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/871319/EU/Design-Operation Continuum Methods for Testing and Deployment under Unforeseen Conditions for Cyber-Physical Systems of Systems/ADEPTNESSen
local.contributor.otherinstitutionOrona, S. Coop.es
local.contributor.otherinstitutionhttps://ror.org/00vn06n10es
local.source.detailspp. 353-358, 2021en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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