Empreu aquest identificador per citar o enllaçar aquest ítem:
http://hdl.handle.net/10609/127057
Registre complet de metadades
Camp DC | Valor | Llengua/Idioma |
---|---|---|
dc.contributor.author | Oliva Navarro, Diego Alberto | - |
dc.contributor.author | Copado Mendez, Pedro Jesus | - |
dc.contributor.author | Hinojosa, Salvador | - |
dc.contributor.author | Panadero Martínez, Javier | - |
dc.contributor.author | Riera Terrén, Daniel | - |
dc.contributor.author | Juan, Angel A. | - |
dc.contributor.other | Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3) | - |
dc.contributor.other | Universitat Oberta de Catalunya (UOC) | - |
dc.contributor.other | Universidad de Guadalajara | - |
dc.contributor.other | Instituto Tecnológico y de Estudios Superiores de Monterrey | - |
dc.date.accessioned | 2021-01-26T10:01:18Z | - |
dc.date.available | 2021-01-26T10:01:18Z | - |
dc.date.issued | 2020-12-18 | - |
dc.identifier.citation | Oliva, D. A., Copado, P. J., Hinojosa, S., Panadero, J., Riera, D., Juan, A. A.(2020). Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios. Mathematics, 8(12). pág. 1-19. doi: 10.3390/math8122240 | - |
dc.identifier.issn | 2227-7390MIAR | - |
dc.identifier.uri | http://hdl.handle.net/10609/127057 | - |
dc.description.abstract | Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy techniques, thus allowing us to tackle optimization problems under a more general scenario, which includes uncertainty elements of both stochastic and non-stochastic nature. After reviewing the related work, the paper discusses, in detail, how the optimization, simulation, and fuzzy components can be efficiently integrated. In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments. The obtained results show that our proposed approach is not only able to generate competitive solutions for the deterministic version of the TOP, but, more importantly, it can effectively solve more realistic TOP versions, including stochastic and other uncertainty elements. | en |
dc.language.iso | eng | - |
dc.publisher | Mathematics | - |
dc.relation.ispartof | Mathematics, 2020, 8(12) | - |
dc.relation.uri | https://doi.org/10.3390/math8122240 | - |
dc.rights | CC BY | - |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0 | - |
dc.subject | simulation-optimization | en |
dc.subject | simheuristics | en |
dc.subject | fuzzy techniques | en |
dc.subject | uncertainty | en |
dc.subject | simulación-optimización | es |
dc.subject | simheurística | es |
dc.subject | técnicas difusas | es |
dc.subject | incertidumbre | es |
dc.subject | simulació-optimització | ca |
dc.subject | simheurística | ca |
dc.subject | tècniques difuses | ca |
dc.subject | incertesa | ca |
dc.subject.lcsh | Algorithms | en |
dc.title | Fuzzy simheuristics: solving optimization problems under stochastic and uncertainty scenarios | - |
dc.type | info:eu-repo/semantics/article | - |
dc.subject.lemac | Algorismes | ca |
dc.subject.lcshes | Algoritmos | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.doi | 10.3390/math8122240 | - |
dc.gir.id | AR/0000008454 | - |
dc.relation.projectID | info:eu-repo/grantAgreement/PID2019-111100RB-C21 | - |
dc.relation.projectID | info:eu-repo/grantAgreement/RED2018-102642-T | - |
dc.relation.projectID | info:eu-repo/grantAgreement/2019-I-ES01-KA103-062602 | - |
dc.type.version | info:eu-repo/semantics/publishedVersion | - |
Apareix a les col·leccions: | Articles cientÍfics Articles |
Arxius per aquest ítem:
Arxiu | Descripció | Mida | Format | |
---|---|---|---|---|
mathematics-08-02240-v2.pdf | 550,36 kB | Adobe PDF | Veure/Obrir |
Comparteix:
Aquest ítem està subjecte a una llicència de Creative Commons Llicència Creative Commons