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Graduate Course Proposal Form Submission Detail - ESI6346

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Current Status: Approved by SCNS - 2015-04-01
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments: For MSIE, PhD in IE, MSEM - Elective. GC appd 2/10/15. To USF Sys 2/27/15. Nmbr 6342 apprd as 6346. Effective 4/1/15

  1. Department and Contact Information

    Tracking Number Date & Time Submitted
    5130 2014-10-28
    Department College Budget Account Number
    Industrial and Management Systems Engineering EN 210300
    Contact Person Phone Email
    Alex Savachkin 8139745577

  2. Course Information

    Prefix Number Full Title
    ESI 6346 Stochastic Decision Models II

    Is the course title variable? N
    Is a permit required for registration? N
    Are the credit hours variable? N
    Is this course repeatable? Y
    If repeatable, how many times? 3

    Credit Hours Section Type Grading Option
    3 C - Class Lecture (Primarily) R - Regular
    Abbreviated Title (30 characters maximum)
    Stochastic Decision Models II
    Course Online? Percentage Online
    C - Face-to-face (0% online) 0


    ESI 6213 Stochastic Decision Models I



    Course Description

    Introduction to modern decision and risk analysis and utility theory. It focuses on the mathematical foundations underlying the quantification and management of risk to support dynamic decision making under uncertainty.

  3. Justification

    A. Please briefly explain why it is necessary and/or desirable to add this course.

    Replacing Selected Topics with Permanent number; already listed in program

    B. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service?

    It is anticipated that future enrollment would be between 7-15 students.

    C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?

    Yes, 1 time

    D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)

    A PhD in Industrial Engineering or equivalent related field is required to teach this course.

  4. Other Course Information

    A. Objectives

    1) Get exposed to the theory of stochastic dynamic programming and Markov decision processes

    2) Build foundations for decision support applications in engineering, healthcare, and finance.

    B. Learning Outcomes

    After successful completion of this course, students will be able to:

    -develop a thorough understanding of the fundamental principles

    -apply modern decision theory and risk analysis.

    C. Major Topics

    I. Review of Markov processes

    (transient analysis; state communication; irreducibility; recurrency; steady state analysis)

    II. Principles of dynamic programming

    (sequential decision making; forward & backward recursion; optimal paths in finite acyclic directed networks; principle of optimality; myopic policies; solving LP problems using DP; applications)

    III. Stochastic dynamic programming

    (finite-stage models; discounted dynamic programming; negative/positive dynamic programming; applications)

    IV. Markov decision processes

    (stationary policies; exhaustive enumeration; policy iteration methods w/ & w/out discounting; value iteration methods; LP solutions; applications)

    V. Elements of risk & utility theory

    (stochastic dominance of reward distributions; concept of utility; utility functions: properties and assessment; certainty equivalence & risk premium; risk attitudes; risk aversion & risk tolerance; common families of utility functions; decreasingly/increasingly/constant risk averse utility functions; multi-attribute utility)

    VI. Approximate dynamic programming

    (various topics; recent advances (time permitting))

    D. Textbooks

    Dynamic probabilistic systems, R. Howard, 2007.

    E. Course Readings, Online Resources, and Other Purchases

    Introduction to stochastic dynamic programming, S. Ross, 1995.

    Decisions with multiple objectives, R. L. Keeney et al., 1993.

    F. Student Expectations/Requirements and Grading Policy

    Grading policy. Three exams will be given each worth 33.33% of the final grade.

    G. Assignments, Exams and Tests

    Two midterms and one final exam. Each is 1/3 of the grade.

    H. Attendance Policy

    Course Attendance at First Class Meeting – Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation – Registration - 4.0101,

    Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (

    In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. It’s the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.

    I. Policy on Make-up Work

    Make up work will be allowed only if prior arrangements with the professor have been made for missing the original due date.

    Policy on academic integrity:

    Violations of academic honesty will be dispatched in accordance with the university policy.

    J. Program This Course Supports

    MSIE and PhD in Industrial Engineering

  5. Course Concurrence Information

    This course could be taken by any engineering discipline at the graduate level as well as Heath Sciences/Medicine and Business if the prerequisite condition is met. Also the MSEM program may use it as an elective with adviser approval.

- if you have questions about any of these fields, please contact or