Stochastic Processes (IE 503)

2021 Fall
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
10.00
Ahmet Barış Balcıoğlu -balcioglu@sabanciuniv.edu,
English
Doctoral, Master
--
Formal lecture
Interactive,Communicative
Click here to view.

CONTENT

Poisson and renewal processes; discrete and continuous Markov chains; applications in queuing, reliability, inventory, production, and telecommunication problems; introduction to queuing networks and network performance analysis.

OBJECTIVE

In this course, we will review the fundamental concepts of the theory of probability and learn about a variety of stochastic processes and we will also discuss some of their applications in engineering. The main objective of this course is to enable students to ?think probabilistically,? and to develop and analyze probability models that capture the effects of randomness on systems under consideration.

LEARNING OUTCOME

The student will be able to model stochastic phenomena in real life (when it applies) as a discrete time or a continuous time Markov chain.

The student will be able to analyze, present, and criticize academic papers involving stochastic processes, queueing models, Markov chains.
The student will link how the probability theory can be employed to model and analyze systems that evolve randomly over time.
The student will learn how a customer arrival process can be modeled as a Poisson process.

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 40
Exam 60

RECOMENDED or REQUIRED READINGS

Textbook

Introduction to Probability Models, Sheldon M. Ross, 9th Edition, 2006,
Academic Press, ISBN-13: 978-0125980623.