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
VL - 187
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
M1 - 115967
ER -