On teaching assistant-task assignment problem: A case study

•We address the problem of assigning teaching assistants to the courses.•Our model focuses mainly on the preferences of the assistants.•Problems of realistic sizes can be solved in a reasonable amount of time.•The model can easily be adapted for the usage of other departments. Teaching assistants (T...

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Veröffentlicht in:Computers & industrial engineering Jg. 79; S. 18 - 26
Hauptverfasser: Güler, M. Güray, Keskin, M. Emre, Döyen, Alper, Akyer, Hasan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.01.2015
Pergamon Press Inc
Schlagworte:
Analytic hierarchy process
Assignment problem
Decision analysis
Deviation
Distribution
Education
Goal programming
Graduates
Higher education planning
Industrial engineering
Mathematical models
Mixed-integer linear programming
Stress concentration
Studies
Tasks
Teaching
Teaching assistant-task assignment problem
Teaching assistants
Higher education planning
Teaching assistant-task assignment problem
Mixed-integer linear programming
Analytic hierarchy process
Goal programming
ISSN:0360-8352, 1879-0550
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Zusammenfassung:•We address the problem of assigning teaching assistants to the courses.•Our model focuses mainly on the preferences of the assistants.•Problems of realistic sizes can be solved in a reasonable amount of time.•The model can easily be adapted for the usage of other departments. Teaching assistants (TAs), together with the senior academic staff, are the centerpiece of university education. TAs are primarily graduate students and they undertake many of the academic and administrative tasks. These tasks are assigned at the beginning of each semester and the objective is to make fair assignments so that the loads are distributed evenly in accordance with requests of the professors and assistants. In this study, a goal programming (GP) model is developed for task assignment of the TAs in an industrial engineering department. While the rules that must be strictly met (e.g., assigning every task to an assistant) are formulated as hard constraints, fair distribution of the loads are modeled as soft constraints. Penalties for deviation from the soft constraints are determined by the Analytic Hierarchy Process (AHP). The proposed GP model avoids assigning the same TA to the same task in several consecutive academic years, i.e., sticking of a task to a TA. We show that the proposed formulation generates better schedules than the previously used ad hoc method with a much less effort.
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ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2014.10.004