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A Time-Saving Algorithm for Team Assignment and Scheduling in a Large-Scale Unit Operations Laboratory Course

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Best Practices for Chemical Engineering Lab-Based Courses

Tagged Division

Chemical Engineering

Page Count

11

Permanent URL

https://peer.asee.org/32014

Download Count

14

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Paper Authors

biography

Andrew Maxson Ohio State University

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Andrew Maxson is an assistant professor of practice in chemical engineering at The Ohio State University where he teaches Chemical Engineering Unit Operations. He earned his B.S. in chemical engineering from Rose-Hulman Institute of Technology and his M.S. and Ph.D. in chemical engineering at Ohio State. Having worked as a manufacturing process engineer for ten years, his focus is on optimizing the process of teaching, as well as hands-on, practical engineering concepts relevant to chemical engineers entering industry.

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Abstract

The chemical engineering program at The Ohio State University has been growing rapidly over the last decade, and it is now one of the largest in the United States. Students enroll in Unit Operations for two semesters, making the course twice the size of other courses in the department; enrollment is typically about 220 students in the combined lab/lecture course. To cope with the growing enrollment, the course instructors have adopted a number of time-saving strategies.

The most successful of these strategies has been the development and use of an evolutionary algorithm to assign students into groups and schedule those groups in the laboratory. The evolutionary algorithm makes random changes to the group assignments and lab schedule and keeps beneficial changes as measured by ten criteria. Data are presented for one recent semester in which the algorithm was successfully used for group assignment and scheduling with an enrollment of 225 students in 48 groups, each completing 2 of 15 experiments available in the lab.

A working example of the algorithm has been made publicly available for download. It was implemented in Microsoft Excel so that no special software installation or licensing is required, and so that anyone familiar with writing worksheet formulas in Excel can easily customize the algorithm for their own use.

The full algorithm (225 students, 48 groups, 12 experiments, and 10 criteria) reached an acceptable solution after about 9 hours of computing time, while the simpler working example provided by the author (72 students, 18 groups, 5 experiments, and 4 criteria) reaches a near optimal solution in about 10 seconds.

Maxson, A. (2019, June), A Time-Saving Algorithm for Team Assignment and Scheduling in a Large-Scale Unit Operations Laboratory Course Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. https://peer.asee.org/32014

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