Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach

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 s...

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Main Authors: Eduardo Canale, Franco Robledo, Pablo Sartor, Luis Stábile
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/14/1/18
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author Eduardo Canale
Franco Robledo
Pablo Sartor
Luis Stábile
author_facet Eduardo Canale
Franco Robledo
Pablo Sartor
Luis Stábile
author_sort Eduardo Canale
collection DOAJ
description 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.
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spelling doaj.art-1b16b8631cd34966abf47b8227a9e7962023-11-23T15:32:06ZengMDPI AGSymmetry2073-89942021-12-011411810.3390/sym14010018Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking ApproachEduardo Canale0Franco Robledo1Pablo Sartor2Luis Stábile3Facultad de Ingeniería, Universidad de la República, Montevideo 11300, UruguayFacultad de Ingeniería, Universidad de la República, Montevideo 11300, UruguayIEEM Business School, Universidad de Montevideo, Montevideo 16000, UruguayFacultad de Ingeniería, Universidad de la República, Montevideo 11300, UruguayStudents 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.https://www.mdpi.com/2073-8994/14/1/18MBA teamsorthogonal regroupingdiversityGRASPVNDpath relinking
spellingShingle Eduardo Canale
Franco Robledo
Pablo Sartor
Luis Stábile
Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
Symmetry
MBA teams
orthogonal regrouping
diversity
GRASP
VND
path relinking
title Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
title_full Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
title_fullStr Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
title_full_unstemmed Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
title_short Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
title_sort solving the max diversity orthogonal regrouping problem by an integer linear programming model and a grasp vnd with path relinking approach
topic MBA teams
orthogonal regrouping
diversity
GRASP
VND
path relinking
url https://www.mdpi.com/2073-8994/14/1/18
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