{"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-simulated-annealing-approach-to-the-student","title":"A simulated annealing approach to the student-project allocation problem","arxiv_id":"1810.11370","date":"2018-10-22","proceeding":null,"authors":["Chown Abigail H.","Cook Christopher J.","Wilding Nigel B."],"abstract":"We describe a solution to the student-project allocation problem using\nsimulated annealing. The problem involves assigning students to projects, where\neach student has ranked a fixed number of projects in order of preference. Each\nproject is offered by a specific supervisor (or supervisors), and the goal is\nto find an optimal matching of students to projects taking into account the\nstudents' preferences, the constraint that only one student can be assigned to\na given project, and the constraint that supervisors have a maximum workload.\nWe show that when applied to a real dataset from a university physics\ndepartment, simulated annealing allows the rapid determination of high quality\nsolutions to this allocation problem. The quality of the solution is quantified\nby a satisfaction metric derived from empirical student survey data. Our\napproach provides high quality allocations in a matter of minutes that are as\ngood as those found previously by the course organizer using a laborious\ntrial-and-error approach. We investigate how the quality of the allocation is\naffected by the ratio of the number of projects offered to the number of\nstudents and the number of projects ranked by each student. We briefly discuss\nhow our approach can be generalized to include other types of constraints and\ndiscuss its potential applicability to wider allocation problems.","url_abs":"http://arxiv.org/abs/1810.11370v1","url_pdf":"http://arxiv.org/pdf/1810.11370v1.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-simulated-annealing-approach-to-the-student","repo_url":"https://github.com/abichown/SPA-Code","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}