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Scientific Programming
Volume 2017, Article ID 9057947, 7 pages
Research Article

Mixed-Integer Linear Programming Models for Teaching Assistant Assignment and Extensions

1School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
2Department of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon, Hong Kong
3School of Economics and Management, Wuhan University, Wuhan 430072, China
4Department of Logistics & Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong
5National Research Council of the National Research Academies of Science, Engineering, and Medicine, Washington, DC 20001, USA
6Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Jiangsu, China

Correspondence should be addressed to Tingsong Wang; nc.ude.uhw@stgnawsme, Shuaian Wang; moc.liamg@naiauhsgnaw, and Zhiyuan Liu; moc.361@uilekael

Received 7 May 2016; Revised 1 November 2016; Accepted 7 December 2016; Published 11 January 2017

Academic Editor: Christoph Kessler

Copyright © 2017 Xiaobo Qu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this paper, we develop mixed-integer linear programming models for assigning the most appropriate teaching assistants to the tutorials in a department. The objective is to maximize the number of tutorials that are taught by the most suitable teaching assistants, accounting for the fact that different teaching assistants have different capabilities and each teaching assistant’s teaching load cannot exceed a maximum value. Moreover, with optimization models, the teaching load allocation, a time-consuming process, does not need to be carried out in a manual manner. We have further presented a number of extensions that capture more practical considerations. Extensive numerical experiments show that the optimization models can be solved by an off-the-shelf solver and used by departments in universities.