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

Qu, X; Yi, W; Wang, T; Wang, S; Xiao, L; Liu, Z

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

Qu, X

Yi, W Wang, T Wang, S Xiao, L Liu, Z

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Publication Type:: Journal Article
Citation:: Scientific Programming, 2017, 2017
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Qu, X https://orcid.org/0000-0003-0973-3756	en_US
dc.contributor.author	Yi, W	en_US
dc.contributor.author	Wang, T	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Xiao, L	en_US
dc.contributor.author	Liu, Z	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Scientific Programming, 2017, 2017	en_US
dc.identifier.issn	1058-9244	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124775
dc.description.abstract	© 2017 Xiaobo Qu et al. 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.	en_US
dc.relation.ispartof	Scientific Programming	en_US
dc.relation.isbasedon	10.1155/2017/9057947	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	Mixed-Integer Linear Programming Models for Teaching Assistant Assignment and Extensions	en_US
dc.type	Journal Article
utslib.citation.volume	2017	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2017	en_US

Abstract:

© 2017 Xiaobo Qu et al. 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.

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http://hdl.handle.net/10453/124775