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Jan 15, 2025
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IE 382 - Stochastic Processes3 lecture hours 0 lab hours 3 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. Queuing theory, Markov processes, and decision theory are examined. Case studies and computer algorithms are utilized. (prereq: MA 262 , junior standing)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• identify and apply quantitative analysis techniques to engineering problems
• use quantitative management technique results to analyze alternative solutions and assist in decision making
• develop an understanding of how these methods impact business and industry
• improve problem solving skills
• improve communication skills
Prerequisites by Topic
• Basic understanding of probability theory and probability distributions
Course Topics
• Introduction to Quantitative Management (1 class)
• Probability Review (2 classes)
• Fundamentals of Decision Theory (3 classes)
• Decision Theory and Utility Theory (3 classes)
• Project Management (3 classes)
• Queuing Theory (5 classes)
• Markov Analysis (3 classes)
• Simulation (2 classes)
• Dynamic Programming (5 classes)
• Review (1 class)
• Examinations (2 classes)
Laboratory Topics
• No laboratory in this course
Coordinator
Aaron Armstrong
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