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
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.


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

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

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


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.