A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance
dc.contributor.author | Sanchez-Anguix, V. | |
dc.contributor.author | Chalumuri, R. | |
dc.contributor.author | Aydoğan, Reyhan | |
dc.contributor.author | Julian, V. | |
dc.date.accessioned | 2020-09-08T06:25:24Z | |
dc.date.available | 2020-09-08T06:25:24Z | |
dc.date.issued | 2019-03 | |
dc.identifier.issn | 1568-4946 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/6916 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/abs/pii/S1568494618306811 | |
dc.description.abstract | 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 studentsupervisor 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. | en_US |
dc.description.sponsorship | Faculty of Engineering and Computing at Coventry University, United Kingdom ; European Commission Joint Research Centre | |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Applied Soft Computing | |
dc.rights | restrictedAccess | |
dc.title | A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance | en_US |
dc.type | Article | en_US |
dc.peerreviewed | yes | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-5260-9999 & YÖK ID 145578) Aydoğan, Reyhan | |
dc.contributor.ozuauthor | Aydoğan, Reyhan | |
dc.identifier.volume | 76 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.identifier.endpage | 15 | en_US |
dc.identifier.wos | WOS:000461145200001 | |
dc.identifier.doi | 10.1016/j.asoc.2018.11.049 | en_US |
dc.subject.keywords | Genetic algorithms | en_US |
dc.subject.keywords | Student-project allocation | en_US |
dc.subject.keywords | Matching | en_US |
dc.subject.keywords | Pareto optimal | en_US |
dc.subject.keywords | Artificial intelligence | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85058435062 | |
dc.contributor.authorFemale | 1 | |
dc.relation.publicationcategory | Article - International Refereed Journal - Institution Academic Staff |
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