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A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance

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A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance

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dc.contributor.author Sanchez-Anguix, Víctor es_ES
dc.contributor.author Chalumuri, Rithin es_ES
dc.contributor.author Aydogan, Reyhan es_ES
dc.contributor.author Julian Inglada, Vicente Javier es_ES
dc.date.accessioned 2020-07-07T03:32:29Z
dc.date.available 2020-07-07T03:32:29Z
dc.date.issued 2019-03 es_ES
dc.identifier.issn 1568-4946 es_ES
dc.identifier.uri http://hdl.handle.net/10251/147518
dc.description.abstract [EN] The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors¿ preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the student¿supervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time. es_ES
dc.description.sponsorship This work is partially supported by funds of the Faculty of Engineering and Computing at Coventry University, United Kingdom, and funds from EU ICT-20-2015 Project SlideWiki granted by the European Commission. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation EC/EU ICT-20-2015 es_ES
dc.relation.ispartof Applied Soft Computing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Genetic algorithms es_ES
dc.subject Student-project allocation es_ES
dc.subject Matching es_ES
dc.subject Pareto optimal es_ES
dc.subject Artificial intelligence es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.asoc.2018.11.049 es_ES
dc.rights.accessRights Embargado es_ES
dc.date.embargoEndDate 2021-03-31 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Sanchez-Anguix, V.; Chalumuri, R.; Aydogan, R.; Julian Inglada, VJ. (2019). A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance. Applied Soft Computing. 76:1-15. https://doi.org/10.1016/j.asoc.2018.11.049 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.asoc.2018.11.049 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 76 es_ES
dc.relation.pasarela S\374203 es_ES
dc.contributor.funder Coventry University es_ES
dc.contributor.funder European Commission es_ES


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