TY - JOUR
T1 - A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance
AU - Sanchez-Anguix, Victor
AU - Chalumuri, Rithin
AU - Aydoğan, Reyhan
AU - Julian, Vicente
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Genetic algorithms
KW - Matching
KW - Pareto optimal
KW - student–project allocation
UR - http://www.scopus.com/inward/record.url?scp=85058435062&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2018.11.049
DO - 10.1016/j.asoc.2018.11.049
M3 - Article
AN - SCOPUS:85058435062
SN - 1568-4946
VL - 76
SP - 1
EP - 15
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
ER -