Meta-Heuristic Solutions to a Student Grouping Optimization Problem
  faced in Higher Education Institutions
  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 stakeholders or software
developers to the most appropriate algorithm for each unique class of NP-hard
problems faced in Higher Education Institutions. To this end, this study
described an optimization problem faced in a real university that involved
grouping students for the presentation of semester results. Ordering based
heuristics, genetic algorithm and the ant colony optimization algorithm
implemented in Python programming language were used to find feasible solutions
to this problem, with the ant colony optimization algorithm performing better
or equal in 75% of the test instances and the genetic algorithm producing
better or equal results in 38% of the test instances.
