Stochastic Processes (IE 503)

2022 Fall
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
10
Ahmet Barış Balcıoğlu balcioglu@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture
Other
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Programs\Type Required Core Elective Area Elective
Business Analytics - With Thesis *
Computer Science and Engineering - With Bachelor's Degree *
Computer Science and Engineering - With Master's Degree *
Computer Science and Engineering - With Thesis *
Cyber Security - With Bachelor's Degree *
Cyber Security - With Master's Degree *
Cyber Security - With Thesis *
Data Science - With Thesis *
Electronics Engineering and Computer Science - With Bachelor's Degree *
Electronics Engineering and Computer Science - With Master's Degree *
Electronics Engineering and Computer Science - With Thesis *
Electronics Engineering - With Bachelor's Degree *
Electronics Engineering - With Master's Degree *
Electronics Engineering - With Thesis *
Energy Technologies and Management-With Thesis *
Industrial Engineering - With Bachelor's Degree *
Industrial Engineering - With Master's Degree *
Industrial Engineering - With Thesis *
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis *
Leaders for Industry Computer Science and Engineering - Non Thesis *
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis *
Leaders for Industry Electronics Engineering - Non Thesis *
Leaders for Industry Industrial Engineering - Non Thesis *
Leaders for Industry Materials Science and Engineering - Non Thesis *
Leaders for Industry Mechatronics Engineering - Non Thesis *
Management Ph.D. - Finance Area - With Bachelor's Degree *
Management Ph.D. - Finance Area - With Master's Degree *
Management Ph.D. - Management and Organization Area - With Bachelor's Degree *
Management Ph.D. - Management and Organization Area - With Master's Degree *
Management Ph.D. - Operations and Supply Chain Management Area - With Bachelor's Degree *
Management Ph.D. - Operations and Supply Chain Management Area - With Master's Degree *
Management Ph.D. - With Bachelor's Degree *
Management Ph.D. - With Master's Degree *
Manufacturing Engineering - Non Thesis *
Manufacturing Engineering - With Bachelor's Degree *
Manufacturing Engineering - With Master's Degree *
Manufacturing Engineering - With Thesis *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) *
Mathematics - With Bachelor's Degree *
Mathematics - With Master's Degree *
Mathematics - With Thesis *
Mechatronics Engineering - With Bachelor's Degree *
Mechatronics Engineering - With Master's Degree *
Mechatronics Engineering - With Thesis *
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) *
Physics - Non Thesis *
Physics - With Bachelor's Degree *
Physics - With Master's Degree *
Physics - With Thesis *

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 OUTCOMES

  • 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 30
Midterm 70

RECOMENDED or REQUIRED READINGS

Textbook

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