TY - JOUR

T1 - Integer linear programming for the Tutor Allocation Problem

T2 - A practical case in a British University

AU - Caselli, Giulia

AU - Delorme, Maxence

AU - Iori, Manuel

N1 - Funding Information:
This research was supported by the Engineering and Physical Science Research Council, United Kingdom through grant No. EP/P029825/1 , and by University of Modena and Reggio Emilia, Italy through grant FAR 2018 .
Publisher Copyright:
© 2021 Elsevier Ltd

PY - 2022/1

Y1 - 2022/1

N2 - In the Tutor Allocation Problem, the objective is to assign a set of tutors to a set of workshops in order to maximize tutors’ preferences. The problem is solved every year by many universities, each having its own specific set of constraints. In this work, we study the tutor allocation in the School of Mathematics at the University of Edinburgh, and solve it with an integer linear programming model. We tested the model on the 2019/2020 case, obtaining a significant improvement with respect to the manual assignment in use and we showed that such improvement could be maintained while optimizing other key metrics such as load balance among groups of tutors and total number of courses assigned. Further tests on randomly created instances show that the model can be used to address cases of broad interest. We also provide meaningful insights on how input parameters, such as the number of workshop locations and the length of the tutors’ preference list, might affect the performance of the model and the average number of preferences satisfied.

AB - In the Tutor Allocation Problem, the objective is to assign a set of tutors to a set of workshops in order to maximize tutors’ preferences. The problem is solved every year by many universities, each having its own specific set of constraints. In this work, we study the tutor allocation in the School of Mathematics at the University of Edinburgh, and solve it with an integer linear programming model. We tested the model on the 2019/2020 case, obtaining a significant improvement with respect to the manual assignment in use and we showed that such improvement could be maintained while optimizing other key metrics such as load balance among groups of tutors and total number of courses assigned. Further tests on randomly created instances show that the model can be used to address cases of broad interest. We also provide meaningful insights on how input parameters, such as the number of workshop locations and the length of the tutors’ preference list, might affect the performance of the model and the average number of preferences satisfied.

KW - Assignment problem

KW - Integer Linear Programming

KW - Matching under preferences

KW - Tutor Allocation Problem

U2 - 10.1016/j.eswa.2021.115967

DO - 10.1016/j.eswa.2021.115967

M3 - Article

AN - SCOPUS:85116811355

SN - 0957-4174

VL - 187

JO - Expert Systems with Applications

JF - Expert Systems with Applications

M1 - 115967

ER -