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Automation and Optimization of Engineering Design Team Selection Considering Personality Types and Course-Specific Constraints

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Collection

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Design as a Social Process: Teams and Organizations

Tagged Division

Design in Engineering Education

Tagged Topic

Diversity

Page Count

14

Page Numbers

26.273.1 - 26.273.14

DOI

10.18260/p.23612

Permanent URL

https://peer.asee.org/23612

Download Count

108

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Paper Authors

biography

Bryony DuPont Oregon State University

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Bryony DuPont is an Assistant Professor in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University (Corvallis, Oregon, USA). Her work looks at the development of computational design tools and optimization algorithms for sustainability, specifically renewable energy systems and sustainable product development. Dr. DuPont and the students of Oregon State’s Design Engineering Lab are currently working on projects that include the layout optimization for wind farms, array design for novel wave energy conversion devices, optimization of collaborative power systems, the sustainable redesign of commuting bicycles, and the quantification of sustainability during the early design phase. Dr. DuPont completed her PhD in Mechanical Engineering from Carnegie Mellon University in 2013 in the Integrated Design Innovation Group, and her projects are currently funded by the National Science Foundation, the National Energy Technology Laboratory, Oregon State University, and Oregon BEST/Bonneville Power Association.

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biography

Christopher Hoyle Oregon State University

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Dr. Christopher Hoyle is currently Assistant Professor and Arthur Hitsman faculty scholar in the area of Design in the Mechanical Engineering Department at Oregon State University. His current research interests are focused upon decision making in engineering design, with emphasis on the early design phase when uncertainty is high and the potential design space is large. His research contributions are to the field of Decision-based Design, specifically in linking consumer preferences and enterprise-level objectives with the engineering design process. He is coauthor of the book Decision-Based Design: Integrating Consumer Preferences into Engineering Design, published in 2012. He received his PhD from Northwestern University in Mechanical Engineering in 2009 and his Master’s degree in Mechanical Engineering from Purdue University in 1994. He was previously a Design Engineer and an Engineering Manager at Motorola, Inc. for 10 years before enrolling in the PhD program at Northwestern University.

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Abstract

THE DESIGN TEAM OPTIMIZER: Automating the Selection of Successful Design Teams Considering Personality Type and Project Preference is home to one of the largest mechanical engineering designgroups in the US, which has been a leader in undergraduate design education. The undergraduatedesign sequence includes a junior-level introductory course and a multi-term senior-levelcapstone course. In the introductory course, students have been placed on design teamsconsidering MBTI personality types for more than twenty years; however, the instructor hasalways performed this team selection process manually. Similarly, in the senior capstone course,students are introduced to a breadth of available research or industry-sponsored projects, andthen are manually placed on teams depending on students’ ranking of their interest in eachproject, as well as their MBTI characteristics. Optimal team selection in introductory and capstone mechanical design courses is vital to thesuccess of the project, and as such, many studies have been conducted to determine the means ofgenerating ideal design teams. This work seeks to employ multiple areas of design team theory,including the use of Myers-Briggs Type Indicators (MBTI) for personality assessment and thecapability for students to be placed in teams with respect to their preference for certain availableprojects, in order to automate the optimization of design team selection. Various test cases areshown that indicate the weighted multi-objective Mixed-Integer Linear Programming approachshown can quickly select optimal design teams that consist of diverse personality types andassign students to preferred projects. This work serves as the first step toward a web-basedautomated design team selection tool that will be made freely available to design researchers andeducators.

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2015 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015