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

Abstract

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. Automation and Optimization of Engineering Design Team Selection Considering Personality Types and Course-Specific Constraints Bryony DuPont and Chris HoyleSchool of Mechanical, Industrial, and Manufacturing Engineering Oregon State University, Corvallis, Oregon, USA Optimal team selection in introductory and capstone mechanical design courses is vital to the success of the project, and, as such, many studies have been conducted to determine the means of generating 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 the capability for students to be placed in teams with respect to certain course-specific constraints, including project preference and teaming constraints, in order to automate the optimization of design team selection. Various test cases are shown that indicate the weighted multi-objective Mixed-Integer Linear Programming approach can quickly select optimal design teams consisting of diverse personality types, and can also assign students to preferred projects. This work serves as the first step toward a downloadable design team selection software package that will be made freely available to design researchers and educators. IntroductionOregon State University is home to one of the largest mechanical engineering design groups in the country, which has historically focused on undergraduate design education that combines both computational and hands-on design skills. As such, Oregon State University's department of mechanical engineering offers multiple undergraduate design courses, including two large project-based courses: a junior-level introductory course, and a multiterm senior-level capstone course. In the junior course, students have been placed on design teams considering MBTI personality types for more than twenty years; however, the instructor and the teaching assistants have always performed this team selection process manually. Simil...

Design Team Performance: A Comparison Between Self-Formed Teams and Teams With Diverse Cognitive Modes

“…Dupont and Hoyle [11] recommend that teams should either have: a) a single leader with a preference for the EN (extraverted intuition) mode, or b) two joint leaders each with a preference for IN (introverted intuition) and EF (extraverted feeling), respectively. While there was not a strong correlation between the leadership score and the EN mode, it was noted that all students with an EN score above 2 were considered leaders within their group.…”