List of all IE courses

3 Credits
Fall term, odd years
Lecture only

ST 314 or equivalent. Students should be familiar with the concepts of discrete and continuous random variables, distribution functions, density functions, probability mass functions, independence, expectation, and variance.
David Kim
424 Rogers Hall

Course Description

This is a first course in modeling stochastic systems with an emphasis on probabilistic reasoning, modeling, and basic solution procedures. This course is a foundation course that should permit the student to pursue (either independently or in courses) more advanced topics in applied stochastic processes, queuing theory.


  • Review of some probability basics.
  • Conditional probability and expectation.
  • Poisson processes and exponential distribution.
  • Poisson processes and exponential distribution – production system application.
  • Discrete and continuous time Markov chains.
  • Discrete and continuous time Markov chains – production system application.
  • Elementary queuing models.
  • Queuing network models (time permitting).

Learning Outcomes

The student, upon completion of this course, will be able to:

  1. Solve a variety of probability problems employing conditional probability and expectation.
  2. Apply properties of the Poisson process and exponential distribution to solve basic stochastic modeling problems.
  3. Understand the basic theory of discrete and continuous time Markov chains and apply this theory to basic stochastic system modeling problems.
  4. Understand the development and application of elementary queuing models.
  5. Apply and solve product form queuing networks (time permitting).