Mathematical programming approach for optimally allocating students' projects to academics in large cohorts

In this article we propose a novel method based on a ranked list of supervisors and categories provided by each student, where a category corresponds to a general research area, incorporating this information into the allocation process. A student's satisfaction may therefore correspond to getting a project either with a highly ranked supervisor and/or in a highly ranked category. With this information, we propose here a systematic approach that relies on a novel mixed-integer linear programming (MILP) model based on a flexible definition of students' satisfaction. Our MILP overcomes the limitations of manual allocation approaches, which when applied to large cohorts are highly time consuming and may produce suboptimal solutions leading to poor satisfaction levels. This MILP has been applied successfully in the School of Chemical Engineering and Analytical Science of The University of Manchester with increased levels of student satisfaction.