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Title: Mixed-integer linear programming models for teaching assistant assignment and extensions
Authors: Qu, X
Yi, W 
Wang, T
Wang, S 
Xiao, L
Liu, Z
Issue Date: 2017
Publisher: Hindawi Publishing Corporation
Source: Scientific programming, 2017, v. 2017, 9057947 How to cite?
Journal: Scientific programming 
Abstract: 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.
ISSN: 1058-9244
EISSN: 1875-919X
DOI: 10.1155/2017/9057947
Rights: 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.
The following article: Qu, X., Yi, W., Wang, T., Wang, S., Xiao, L., & Liu, Z. (2017). Mixed-integer linear programming models for teaching assistant assignment and extensions. Scientific Programming, 2017, is available at https//
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