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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International
dc.contributor.authorOchoa, William
dc.contributor.authorLegaristi Labajos, Jon
dc.contributor.authorLarrinaga, Felix
dc.contributor.authorPerez Riaño, Alain
dc.date.accessioned2024-02-02T08:53:21Z
dc.date.available2024-02-02T08:53:21Z
dc.date.issued2024
dc.identifier.issn1872-7115
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174036
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6249
dc.description.abstractThe manufacturing industry of the future requires innovative approaches to optimize operational efficiency and adaptability. Integrating context-awareness into workflow management systems has emerged as a promising avenue to enhance efficiency in modern manufacturing processes. This research presents an innovative context-aware workflow management architecture designed to address industry-related challenges and overcome current limitations in the state-of-the-art. The architecture leverages Industry 4.0 standards for asset representation and workflow notation while incorporating a Context Analyzer component for real-time context interpretation. The effectiveness of the proposed solution is demonstrated in a real-world manufacturing setting, specifically in the scenario of collecting work order materials using the Robot Operating System (ROS) technology for robot navigation. The evaluation showcases improvements in task completion rate, resource utilization, and task completion time. These outcomes exemplify the potential benefits of incorporating context-awareness into manufacturing workflows, providing insights for further improvements. Contributions include advancing the understanding of context-aware workflow management, a review of the challenges that cap its adoption in the manufacturing domain, a qualitative comparison of similar approaches, practical implementation of the proposed architecture, evaluation of the context-aware component, and provision of the source code and datasets to the community for future advancement and reproducibility.en
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritza
dc.language.isoeng
dc.publisherElsevier
dc.rights© 2023 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceFuture Generation Computer Systems
dc.subjectContext-aware
dc.subjectworkflow management
dc.subjectODS 9 Industria, innovación e infraestructura
dc.subjectODS 12 Producción y consumo responsables
dc.subjectIndustry 4.0
dc.subjectRobot Operating System (ROS)
dc.subjectBusiness Process Modeling and Notation (BPMN)
dc.subjectAsset Administration Shell (AAS)
dc.titleDynamic context-aware workflow management architecture for efficient manufacturing: A ROS-based case study
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2
local.contributor.groupIngeniería del software y sistemas
local.contributor.groupRobótica y automatización
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.1016/j.future.2023.12.024
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Elkartek 2022/KK-2022-00007/CAPV/SIIRSE project/SIIRSE
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2022-2023/IT1519-22/CAPV/Ingeniería de Software y Sistemas/
local.source.detailsVol. 153. Pp. 505-520
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85


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