ME575/CE575: Optimization Techniques in Engineering (3 credit hours). This course covers theory and applications for optimization in engineering design. Topics include:
- Optimization Introduction
- Mathematical Modeling
- Unconstrained Optimization
- Discrete Optimization
- Genetic Algorithms
- Constrained Optimization
- Robust Optimization
- Dynamic Optimization
Both MATLAB and Python are used throughout the course as computational tools for implementing homework and exam problems and for the course projects. Tutorials in MATLAB and Python are provided as part of a separate computational tools course.
Professor: John D. Hedengren
Office: 801-422-2590, 350R CB Cell: 801-477-7341 Contact: john.hedengren [at] byu.edu
John Hedengren worked 5 years with ExxonMobil Chemical on Optimization solutions for the petrochemical industry. He conducts research in optimization methods, modeling systems, and applications in Chemical Engineering. The PRISM group is actively working on oil and gas drilling automation, reservoir engineering, process optimization, unmanned aerial vehicles, and systems biology.
Everyone will have access to the book (download PDFs). You will need to thoroughly understand everything in the chapters. Please read the appropriate section before coming to class as indicated on the schedule.
- Belegundu A. and T. Chandrupatla Optimization Concepts and Applications in Engineering, Prentice Hall, 1999.
- Gen, M. and R. Cheng, Genetic Algorithms and Engineering Optimization, Wiley, 2000.
- Edgar, T.F., Himmelblau, D.M., and L.S. Lasdon, Optimization of Chemical Processes, McGraw Hill, 2001. Download PDF
- Fletcher R., Practical Methods of Optimization Volumes 1,2, John Wiley 1980, 1981.
- Luenberger and Ye, Linear and Nonlinear Programming Third Edition, Springer, 2008.
There is also another engineering optimization course taught by Dr. Andrew Ning of the Mechanical Engineering department. Dr. Ning has another optimization textbook that is very good and focuses on aerospace engineering examples.
As needed through-out the semester. The Teaching Assistants will conduct the recitation sessions. Generally they will be held:
- Before exams
- To help work through difficult project issues
- For additional class time
ME 575: Optimization Techniques in Engineering (3 credit hours). Also cross-listed as CE EN 575. Application of computer optimization techniques to constrained engineering design. Theory and application of unconstrained and constrained nonlinear algorithms. Genetic algorithms. Robust design methods. Prerequisite: MATH 302; C, C++, or similar computer language.
30% (15% Each)
Reading is essential to succeeding in this class. There are a number of resources that are available on this web-site or through external sources.
Unannounced quizzes will be given on the assigned reading material for that day. The number of quizzes will increase as student preparation for classes decreases. Motto: BE PREPARED! Quizzes will not be rescheduled, and extra credit is not available. Quizzes count for a homework grade each. The quizzes are intended to: 1) provide an opportunity for you to practice responding to questions under time pressure, 2) provide encouragement for you to keep up with the course material, 3) encourage attendance.
There will be a mid-term and the final exam. These exams may be closed book and/or open book, in-class or in the testing center, as specified by the instructor prior to the exam. Exams will only be given after the scheduled date by special permission. Students with conflicts should arrange to take the exam prior to the scheduled date.
You will be required to complete two group projects. Groups will consist of 3 students and one report will be submitted for the group. Each group member is to fully participate. I will provide suggestions or you can do something of your own interest or something that is integrated with a campus or off-campus research project.
One of the most common questions that I receive from students who would like to take this class is, "How much programming experience is required to succeed in the class?"
To address this concern, we have prepared software tutorials that assume very little knowledge of programming. There are also many excellent resources on the internet that give tutorial introductions to programming. Those students who have no or little programming experience can review these step-by-step instructional videos to gain some of the required background. We can also hold recitation sessions in a computer lab outside of normal class times if there is need.
This is an optimization course, not a programming course, but some familiarity with MATLAB, Python, C++, or equivalent programming language is required to perform assignments, projects, and exams. Students who complete the course will gain experience in at least one of these programming languages.
I will come prepared to each class, ready to help explain the material covered in the reading. I appreciate attentive students who respect my time and the time of other students.
A Read material in advance, be attentive and ask questions in lectures, understand and do all homework on time, study hard for exams well before the exam starts, work hard and perform well on exams and the class projects.
B Skim material in advance, attend lectures and try to stay awake, depend on TA for homework help, casually study for the exam by working the practice exam instead of learning concepts.
C Never read book, work on other homework during class, skip some homework assignments, start cramming for the exam the night before the exam.
D Skip class, don't turn in homework or turn it in late, start learning during the exam.
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