Comparing Outcomes Between Two Engineering Majors in a Deterministic Operations Research Course
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
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Multidisciplinary Engineering Division (MULTI) Technical Session 5
- Tagged Division
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Multidisciplinary Engineering Division (MULTI)
- Permanent URL
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https://peer.asee.org/48483
Paper Authors
Hsin-Li Chan Penn State University
Dr. Hsin-Li Chan is an Assistant Teaching Professor of Industrial Engineering at Penn State Behrend. She received the Ph.D. degree in Industrial Engineering from Clemson University and the M.S. in Applied Statistics from Syracuse University. Dr. Chan’s research interests include applied statistics, quality control in manufacturing process, and optimization.
Yuan-Han Huang Penn State University
Dr. Yuan-Han Huang is an Associate Professor of Industrial Engineering and graduate faculty for the Master of Manufacturing Management (MMM) program at Penn State Behrend. He received the B.S. in Industrial Engineering from I-Shou University, Taiwan; the M.B.A. in Industrial Management from the National Taiwan University Science & Technology, Taiwan; and the M.S. in Industrial & Systems Engineering from the State University of New York (SUNY), Buffalo. Dr. Huang received his Ph.D. in Industrial Engineering with a concentration in Human Factors Engineering from Clemson University in 2013.
Barukyah Shaparenko Penn State University
Dr. Barukyah Shaparenko is an Assistant Teaching Professor of Mechanical Engineering at Penn State Erie, The Behrend College. He received a B.S. in Mechanical Engineering from Penn State University in 2009 and a Ph.D. in Mechanical Engineering from the University of Pennsylvania in 2015. He has taught classes in mechanical engineering (thermodynamics, fluid mechanics, heat transfer, CFD, measurements, freshman engineering design), engineering mechanics (statics, strength of materials), computer science (MATLAB programming), biomedical engineering (measurements), and math (calculus I and II).
Abstract
In an introductory undergraduate-level deterministic modeling course that covers linear and integer programming, students apply modeling and optimization approaches to address challenges related to network flows, project management, transportation, and assignment problems. They also acquire proficiency in various solution strategies, including the simplex method and the branch-and-bound approach. Duality and sensitivity analysis are comprehensively covered, as well as their economic interpretations. Both Industrial Engineering and Mechanical Engineering students share access to identical learning modules, completing the same assignments and exams. The objective of this study is to compare the performance of these two student cohorts and assess their abilities in three crucial areas: proficiency in matrix algebra, the capacity to identify and formulate engineering problems, and the ability to solve and interpret these problems. The study's findings reveal that Mechanical Engineering students outperformed their counterparts overall. This group demonstrated stronger skills in comprehensively understanding the problem scope and formulating problems into mathematical models. Additionally, the study underscores the significance of applied examples, serving as a crucial bridge connecting theoretical understanding with practical application. This pedagogical approach fosters a deeper comprehension of the subject matter, proving beneficial for students across various engineering disciplines.
Chan, H., & Huang, Y., & Shaparenko, B. (2024, June), Comparing Outcomes Between Two Engineering Majors in a Deterministic Operations Research Course Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48483
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