A Preference-Based Faculty-Assignment Tool for Course Scheduling Optimization

Sami Khorbotly; Daniel White

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: DSA Technical Session 4
Tagged Topic: Data Science & Analytics Constituent Committee (DSA)
Permanent URL: https://peer.asee.org/46476

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Paper Authors

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Sami Khorbotly Valparaiso University

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Received the Bachelor of Engineering degree in Electrical Engineering from Beirut Arab University, Beirut, Lebanon in 2001. Received the M.S. and Ph. D. degrees both in Electrical and Computer Engineering from the University of Akron, Akron, OH in 2003 and 2007, respectively.
Currently serves as a Chair and Professor of Electrical and Computer Engineering at Valparaiso University.

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Daniel White Valparaiso University orcid.org/0000-0001-5560-2496

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Daniel J. White is an Associate Professor in Electrical and Computer Engineering at Valparaiso University's College of Engineering, joining as an Instructor in 2013. He received the B.S. EE and M.S. EE in 2005 and 2006, respectively, and the Ph.D. in Ele

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Abstract

Course scheduling is one of the most time-consuming tasks that department chairs have to perform every academic semester. The course scheduling problem includes assigning a faculty member, the course time/day(s), and a classroom for each offered course. Course scheduling is an NP-complete problem that has been extensively studied over the years. In this work, rather than addressing the course scheduling problem as a whole, we tackle only the faculty assignment side of the problem. This “divide and conquer” approach reduces the task from an NP-complete problem to a problem for which an optimal solution exists. The goal here is to prioritize assigning the best possible faculty member for each course. Once that is accomplished, class times and locations can be later assigned using other tools. Faculty assignment is prioritized because we believe that no factor is nearly as important as having the most suitable professor teach each course. The time of a qualified professor is by far the most valuable academic resource, above the limited time slots and the limited spatial resources available on campus. Our faculty-assignment optimization tool uses Linear Programming (LP) with the objective function being the maximization of the overlap between the courses to be offered in a semester and the faculty members’ preferences and skills. This maximizes the chances of every faculty member teaching courses they are interested in. A set of constraints is created to ensure the full coverage of all courses/sections to be offered and also to ensure that no faculty member is assigned to teach more than a pre-determined teaching load limit. The tool is embedded in a web-based application and is available for the public to use. One of the greatest features of the tool is its objectivity. It generates the faculty-course assignments based on the faculty preferences. It does not favor one faculty member over the other. Disgruntled faculty members who are not pleased with the outcome can no longer be upset with the chair of the department. Additionally, the tool also helps identify structural holes in the department’s depth of coverage across topics, prompting strategic staffing discussions and guiding future faculty searches. The paper explains how to use the tool and includes some scheduling results for the sake of demonstration. The paper also includes a link for interested future users to access the free, web-based version of the tool to find optimized solutions to their scheduling problems.

Citation
Format

Khorbotly, S., & White, D. (2024, June), A Preference-Based Faculty-Assignment Tool for Course Scheduling Optimization Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46476

TY  - CPAPER
AB  - Course scheduling is one of the most time-consuming tasks that department chairs have to perform every academic semester. The course scheduling problem includes assigning a faculty member, the course time/day(s), and a classroom for each offered course. Course scheduling is an NP-complete problem that has been extensively studied over the years.
In this work, rather than addressing the course scheduling problem as a whole, we tackle only the faculty assignment side of the problem. This “divide and conquer” approach reduces the task from an NP-complete problem to a problem for which an optimal solution exists. The goal here is to prioritize assigning the best possible faculty member for each course. Once that is accomplished, class times and locations can be later assigned using other tools. Faculty assignment is prioritized because we believe that no factor is nearly as important as having the most suitable professor teach each course. The time of a qualified professor is by far the most valuable academic resource, above the limited time slots and the limited spatial resources available on campus. 
Our faculty-assignment optimization tool uses Linear Programming (LP) with the objective function being the maximization of the overlap between the courses to be offered in a semester and the faculty members’ preferences and skills. This maximizes the chances of every faculty member teaching courses they are interested in. A set of constraints is created to ensure the full coverage of all courses/sections to be offered and also to ensure that no faculty member is assigned to teach more than a pre-determined teaching load limit. The tool is embedded in a web-based application and is available for the public to use.
One of the greatest features of the tool is its objectivity. It generates the faculty-course assignments based on the faculty preferences. It does not favor one faculty member over the other. Disgruntled faculty members who are not pleased with the outcome can no longer be upset with the chair of the department. Additionally, the tool also helps identify structural holes in the department’s depth of coverage across topics, prompting strategic staffing discussions and guiding future faculty searches.
The paper explains how to use the tool and includes some scheduling results for the sake of demonstration. The paper also includes a link for interested future users to access the free, web-based version of the tool to find optimized solutions to their scheduling problems.

AU  - Sami Khorbotly
AU  - Daniel White
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - A Preference-Based Faculty-Assignment Tool for Course Scheduling Optimization
UR  - https://peer.asee.org/46476
ER  -