dc.contributor.author | Shavazipour, Babooshka | |
dc.contributor.author | López-Ibáñez, Manuel | |
dc.contributor.author | Miettinen, Kaisa | |
dc.date.accessioned | 2021-07-20T05:30:38Z | |
dc.date.available | 2021-07-20T05:30:38Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Shavazipour, B., López-Ibáñez, M., & Miettinen, K. (2021). Visualizations for Decision Support in Scenario-based Multiobjective Optimization. <i>Information Sciences</i>, <i>578</i>, 1-21. <a href="https://doi.org/10.1016/j.ins.2021.07.025" target="_blank">https://doi.org/10.1016/j.ins.2021.07.025</a> | |
dc.identifier.other | CONVID_98930281 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/77178 | |
dc.description.abstract | We address challenges of decision problems when managers need to optimize several conflicting objectives simultaneously under uncertainty. We propose visualization tools to support the solution of such scenario-based multiobjective optimization problems. Suitable graphical visualizations are necessary to support managers in understanding, evaluating, and comparing the performances of management decisions according to all objectives in all plausible scenarios. To date, no appropriate visualization has been suggested. This paper fills this gap by proposing two visualization methods: a novel extension of empirical attainment functions for scenarios and an adapted version of heatmaps. They help a decision-maker in gaining insight into realizations of trade-offs and comparisons between objective functions in different scenarios. Some fundamental questions that a decision-maker may wish to answer with the help of visualizations are also identified. Several examples are utilized to illustrate how the proposed visualizations support a decision-maker in evaluating and comparing solutions to be able to make a robust decision by answering the questions. Finally, we validate the usefulness of the proposed visualizations in a real-world problem with a real decision-maker. We conclude with guidelines regarding which of the proposed visualizations are best suited for different problem classes. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Elsevier BV | |
dc.relation.ispartofseries | Information Sciences | |
dc.rights | CC BY 4.0 | |
dc.subject.other | multi-dimensional visualization | |
dc.subject.other | scenario-based multi-criteria optimization | |
dc.subject.other | MCDM | |
dc.subject.other | scenario planning | |
dc.subject.other | uncertainty | |
dc.subject.other | empirical attainment function | |
dc.title | Visualizations for Decision Support in Scenario-based Multiobjective Optimization | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202107204352 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Multiobjective Optimization Group | fi |
dc.contributor.oppiaine | Laskennallinen tiede | fi |
dc.contributor.oppiaine | Päätöksen teko monitavoitteisesti | fi |
dc.contributor.oppiaine | Multiobjective Optimization Group | en |
dc.contributor.oppiaine | Computational Science | en |
dc.contributor.oppiaine | Decision analytics utilizing causal models and multiobjective optimization | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 1-21 | |
dc.relation.issn | 0020-0255 | |
dc.relation.volume | 578 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2021 The Authors. Published by Elsevier Inc. | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.grantnumber | 287496 | |
dc.relation.grantnumber | 322221 | |
dc.subject.yso | monitavoiteoptimointi | |
dc.subject.yso | mallit (mallintaminen) | |
dc.subject.yso | optimointi | |
dc.subject.yso | haasteet (ongelmat) | |
dc.subject.yso | skenaariot | |
dc.subject.yso | visualisointi | |
dc.subject.yso | päätöksenteko | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p32016 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p510 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p13477 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p6564 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p3296 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p7938 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p8743 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.1016/j.ins.2021.07.025 | |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Suomen Akatemia | fi |
dc.relation.funder | Suomen Akatemia | fi |
jyx.fundingprogram | Academy Project, AoF | en |
jyx.fundingprogram | Academy Project, AoF | en |
jyx.fundingprogram | Akatemiahanke, SA | fi |
jyx.fundingprogram | Akatemiahanke, SA | fi |
jyx.fundinginformation | This research was partly funded by the Academy of Finland (grants no. 287496 and 322221). This research is also related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, jyu.fi/demo) of the University of Jyvaskyla. M. López-Ibáñez is a “BeatrizGalindo” Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Spanish Ministry of Science andInnovation (MICINN). | |
dc.type.okm | A1 | |