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

Name: A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance
Author: Sanchez-Anguix, V., Chalumuri, R., Aydoğan, Reyhan, Julian, V.

İsim	A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance
Yazar	Sanchez-Anguix, V., Chalumuri, R., Aydoğan, Reyhan, Julian, V.
Basım Tarihi:	2019-03
Basım Yeri	- Elsevier
Konu	Genetic algorithms, Student-project allocation, Matching, Pareto optimal, Artificial intelligence
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1568-4946
Kayıt Numarası	3f6ed081-8ba5-4032-afb1-1a8808796ad1
Lokasyon	Computer Science
Tarih	2019-03
Notlar	Faculty of Engineering and Computing at Coventry University, United Kingdom ; European Commission Joint Research Centre
Örnek Metin	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.
DOI	10.1016/j.asoc.2018.11.049
Cilt	76

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

Yazar Sanchez-Anguix, V., Chalumuri, R., Aydoğan, Reyhan, Julian, V.

Basım Tarihi 2019-03

Basım Yeri - Elsevier

Konu Genetic algorithms, Student-project allocation, Matching, Pareto optimal, Artificial intelligence

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1568-4946

Kayıt Numarası 3f6ed081-8ba5-4032-afb1-1a8808796ad1

Lokasyon Computer Science

Tarih 2019-03

Notlar Faculty of Engineering and Computing at Coventry University, United Kingdom ; European Commission Joint Research Centre

Örnek Metin 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.

DOI 10.1016/j.asoc.2018.11.049

Cilt 76