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dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire Génie Industriel - EA 2606 [LGI]
dc.contributor.authorABI AKLE, Audrey
ORCID: 0000-0003-2998-5862
IDREF: 189052856
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorMINEL, Stephanie
hal.structure.identifierLaboratoire Génie Industriel - EA 2606 [LGI]
dc.contributor.authorYANNOU, Bernard
dc.contributor.editorChristian Weber
dc.contributor.editorStephan Husung
dc.contributor.editorMarco CantaMESsa
dc.contributor.editorGaetano Cascini
dc.contributor.editorDorian Marjanovic
dc.contributor.editorSerena Graziosi
dc.date.accessioned2024-02-03T11:04:29Z
dc.date.available2024-02-03T11:04:29Z
dc.date.conference2015-07-27
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/187776
dc.description.abstractEnDesign space exploration, that is an embodiment of a paradigm Design by Shopping, refers to the systematic activity of discovery and evaluation of the elements in the design space in order to identify optimal solution by reducing the design space toward an area of performance. This activity is composed of three main phases: the discovery, the optimization and the selection. There are existing tools for the design space exploration with different graphs (ScatterPlot matrix, 2D and 3D scatter plot, parallel coordinates plot, etc.). These graphs are useful for the representation of multidimensional set of data with an unlimited number of alternatives (design points). Obviously, during the selection phase, designers face to a reduced design space with a limited number of design points (in a performance area). Thus, in our work, we try to identify which graph is the most adapted to the selection phase. It emerges, from literature, three graphs useful for the representation of multidimensional set of data (>3 variables) and with a limited number of alternatives (<50). Thus we have designed experimentation composed of 3 scenarios (with 13 design parameters and 5 variables of performance) performed by 30 participants. It results one graph more suited to the selection phase in the Design by Shopping: the Parallel Coordinates Plot.
dc.language.isoENen_US
dc.subject.envisualization
dc.subject.enComputer aided design (CAD)
dc.subject.enDecision making
dc.title.enGraphical support adapted to designers for the selection of an optimal solution in Design by Shopping
dc.typeCommunication dans un congrèsen_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.page215-224en_US
bordeaux.volumeDesign Methods and Tools - part 2en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.conference.titleInternational Conference on Engineering Design (ICED 15)en_US
bordeaux.countryiten_US
bordeaux.title.proceedingProceedings of the 20th International Conference on Engineering Design (ICED15)en_US
bordeaux.conference.cityMilanen_US
bordeaux.import.sourcehal
hal.identifierhal-01244791
hal.version1
hal.invitednonen_US
hal.proceedingsouien_US
hal.conference.end2015-07-30
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&amp;rft_val_fmt=info:ofi/fmt:kev:mtx:journal&amp;rft.volume=Design%20Methods%20and%20Tools%20-%20part%202&amp;rft.spage=215-224&amp;rft.epage=215-224&amp;rft.au=ABI%20AKLE,%20Audrey&amp;MINEL,%20Stephanie&amp;YANNOU,%20Bernard&amp;rft.genre=unknown


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