Syllabus
Rutgers University
College of Engineering
Department of Industrial Engineering
540:505 Decision Making under Uncertainty
Instructor: Dr. M. B. Gürsoy
Office: CoRE 218
Phone: X-5465
Email: gursoy@soe.rutgers.edu
COURSE OUTLINE
 
Topics to be covered:
    1. Introduction to probability theory and random variables.

    2. Discrete and continuous probability distributions.

    3. Conditional probability and moments.

    4. Discrete-time Markov Chains: Absorbing Chains, First Step Analysis.

    5. Discrete-time Markov Chains: Ergodic Chains, Steady-State Analysis.

    6. Poisson Processes.

    7. Continuous-time Markov Chains and Birth-Death Queues.

    8. Basics of Risk Modeling and Analysis.

    9. Uncertainty characterization using scenarios & decision trees.

    10. Basics of stochastic programming.

    11. Applications and Case Studies in energy, transportation and production systems.

Text:

“An introduction to Stochastic Modeling", H. M. Taylor and S. Karlin, 3rd Edition, Academic Press 1998

References:
    1. “Introduction to Probability Models”, Sheldon Ross, AP, 9th Ed., 2007.

    2. “Stochastic Modeling and the Theory of Queues”, Ronald Wolff, Prentice Hall, 1989.

Grading:

30% Midterm Exam, 30% HWs and Project, 40% Final Exam

※ All exams are closed book closed notes, no electronic or hard copy cheat sheets are allowed. Please go over the Rutgers Academic Integrity Policy.