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

• Published in: Applied soft computing Vol. 76; pp. 1 - 15
• Main Authors: Sanchez-Anguix, Victor, Chalumuri, Rithin, Aydoğan, Reyhan, Julian, Vicente
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2019
• Subjects: Artificial intelligence; Genetic algorithms; Matching; Pareto optimal; student–project allocation; Matching; student–project allocation; Pareto optimal; Artificial intelligence; Genetic algorithms

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 abilitie...