Course Context: The analyses and improvement of engineering systems and industrial production processes through experiments are typically beset with incomplete mathematical knowledge of their underlying workings and the presence of statistical uncertainties when taking investigative data. Students will learn a highly effective set of approaches for handling optimization problems regularly occurring in these situations.
Course Summary: In the first half of the course you will build up your capabilities for solving such problems by learning several first and second order mathematical optimization approaches that have been found to be effective for such problems and some metaheuristic approaches, including genetic algorithms and artificial neural networks, that have recently shown considerable promise for these types of problems. In the second half of the course you will apply these approaches to a variety of structured multidimensional problems. You will also learn how to efficiently use data to construct the necessary mathematical models.
This class is offered online in a webinar-style format, and can be accessed synchronously or asynchronously. Synchronous means you can log on live and participate in the class as it is occurring on campus, but participation in this format is not mandatory. Asynchronous means you can access the video recording of the class sessions whenever it is convenient for you.
This course will not have formal exams, but will have several challenging homework assignments.
Recommended References: Turban, E. and J. Aronson, Decision Support Systems and Intelligent Systems, Prentice-Hall, 2005 [ISBN 0-13-089465-6], Tsoukalas, L. and R. Uhrig, Fuzzy and Neural Approaches in Engineering, John Wiley & Sons, 1997 [ISBN 0-471-16003-2] and “An Introduction to Optimization”, 4th edition by Edwin K. P. Chong and Stanislaw H. Zak, John Wiley & Sons, 2013, ISBN 978-1-1182-7901-4.
This course can be applied towards:
STAT 315 (Statistics for Engineers and Scientists) or equivalent.
Military personnel admitted to a College of Engineering online degree program may be eligible for a 15% tuition discount. Tuition discounts can only be given if you provide the appropriate discount code at the time of registration. Call (877) 491-4336 or email
Textbook and Materials
No textbook required. Handouts will be provided.
Software: Minitab is available by remote access to CSU's Engineering Network Services. Academic pricing is available through CSU RAMtech and trial subscriptions may be available from the supplier.