IE 3820 - Stochastic Processes

4 lecture hours 0 lab hours 4 credits

Course Description
This course continues the modeling approach to problem solving by presenting techniques used to analyze and design systems affected by random variables. Queueing theory, Markov processes, and decision theory are examined. Case studies and computer algorithms are utilized. (prereq: MA 262 and (MA 2314 or MA 231))

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Identify and apply quantitative analysis techniques to engineering problems related to random processes
Use quantitative management technique results to analyze alternative solutions and assist in decision making
Have an understanding of how these methods impact business and industry
Demonstrate systematic problem-solving skills and be able to communicate the process effectively

Prerequisites by Topic

Understanding of basic probabilistic principles and calculations
Familiarity with common discrete probability distributions
An ability to take complex derivatives and limits

Course Topics

Introduction to quantitative management
Probability for stochastic processes
Fundamentals of decision theory
Decision theory and utility theory
Queueing theory
Markov analysis
Dynamic programming
Review
Examinations

Coordinator
Dr. Aaron Armstrong

Announcement

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Milwaukee School of Engineering

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IE 3820 - Stochastic Processes