{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-near-pareto-optimal-approach-to-student","title":"A near Pareto optimal approach to student-supervisor allocation with two sided preferences and workload balance","arxiv_id":"1812.06474","date":"2018-12-16","proceeding":null,"authors":["Victor Sanchez-Anguix","Rithin Chalumuri","Reyhan Aydogan","Vicente Julian"],"abstract":"The problem of allocating students to supervisors for the development of a\npersonal project or a dissertation is a crucial activity in the higher\neducation environment, as it enables students to get feedback on their work\nfrom an expert and improve their personal, academic, and professional\nabilities. In this article, we propose a multi-objective and near Pareto\noptimal genetic algorithm for the allocation of students to supervisors. The\nallocation takes into consideration the students and supervisors' preferences\non research/project topics, the lower and upper supervision quotas of\nsupervisors, as well as the workload balance amongst supervisors. We introduce\nnovel mutation and crossover operators for the student-supervisor allocation\nproblem. The experiments carried out show that the components of the genetic\nalgorithm are more apt for the problem than classic components, and that the\ngenetic algorithm is capable of producing allocations that are near Pareto\noptimal in a reasonable time.","url_abs":"http://arxiv.org/abs/1812.06474v1","url_pdf":"http://arxiv.org/pdf/1812.06474v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-near-pareto-optimal-approach-to-student","repo_url":"https://github.com/rithinch/GeneticAlgorithm-StudentSupervisorAllocation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-near-pareto-optimal-approach-to-student","repo_url":"https://github.com/rithinch/Student-Supervisor-Allocation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-near-pareto-optimal-approach-to-student","repo_url":"https://github.com/rithinch/pareto-optimal-student-supervisor-allocation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}