Abstract
Students from Master of Business Administration (MBA) programs are usually split into teams. In light of the generalistic nature of MBA programs, diversity within every team is desirable in terms of gender, major, age and other criteria. Many schools rotate the teams at the beginning of every term so that each student works with a different set of peers during every term, thus training his or her adaptation skills and expanding the peer network. Achieving diverse teams while avoiding–or minimizing—the repetition of student pairs is a complex and time-consuming task for MBA Directors. We introduce the Max-Diversity Orthogonal Regrouping (MDOR) problem to manage the challenge of splitting a group of people into teams several times, pursuing the goals of high diversity and few repetitions. We propose a hybrid Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Descent (GRASP/VND) heuristic combined with tabu search and path relinking for its resolution, as well as an Integer Linear Programming (ILP) formulation. We compare both approaches through a set of real MBA cohorts, and the results show that, in all cases, the heuristic approach significantly outperforms the ILP and manually formed teams in terms of both diversity and repetition levels.
Highlights
We focused on maximum-diversity regrouping assignments of Master of Business Administration (MBA) students; the reader can find potential applications to similar partitioning problems that involve rotating the partitions several times
It was conceived to cope with the problem of partitioning MBA cohorts into high-diversity teams, rotating the teams every term and keeping repetitions under a given threshold
An exact integer linear programming method and a GRASP/VND methodology enriched with path relinking are proposed in order to address the Max-Diversity Orthogonal Regrouping (MDOR)
Summary
Based on our scientific literature review, the works that are closest to ours are [4,5,6]. This work applied integer linear programming, and it is equivalent to the min-sum approach given by [5]. In [10], a competitive General Variable Neighborhood Search (GVNS) was proposed An extension of this GVNS was developed in [11], with a skewed VNS combined with a shaking process to better explore the search space. It is worth remarking that, this work was motivated by the assignment of MBA students to teams that are reconstructed every term, it has potential applications to other scenarios, such as staffing and scheduling in workforce management [13], team formation models for collaboration [14] and team-formation algorithms for faultline minimization [15], among others
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