Meta-Heuristic Solutions to a Student Grouping Optimization Problem faced in Higher Education Institutions
Patrick Kenekayoro, Biralatei Fawei

TL;DR
This paper explores heuristic algorithms like genetic algorithms and ant colony optimization to solve a student grouping problem in higher education, demonstrating their effectiveness through empirical testing on real university data.
Contribution
It introduces a novel student grouping optimization problem in higher education and evaluates heuristic solutions, highlighting the applicability of meta-heuristics to diverse NP-hard problems.
Findings
Ant colony optimization outperforms or matches other heuristics in 75% of cases.
Genetic algorithm performs better or equal in 38% of test instances.
Heuristic methods are effective for complex student grouping problems.
Abstract
Combinatorial problems which have been proven to be NP-hard are faced in Higher Education Institutions and researches have extensively investigated some of the well-known combinatorial problems such as the timetabling and student project allocation problems. However, NP-hard problems faced in Higher Education Institutions are not only confined to these categories of combinatorial problems. The majority of NP-hard problems faced in institutions involve grouping students and/or resources, albeit with each problem having its own unique set of constraints. Thus, it can be argued that techniques to solve NP-hard problems in Higher Education Institutions can be transferred across the different problem categories. As no method is guaranteed to outperform all others in all problems, it is necessary to investigate heuristic techniques for solving lesser-known problems in order to guide…
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Taxonomy
TopicsScheduling and Timetabling Solutions · Vehicle Routing Optimization Methods · Resource-Constrained Project Scheduling
