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Graduate Course Proposal Form Submission Detail - ESI6346
Tracking Number - 5130

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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

Detail Information

  1. Date & Time Submitted: 2014-10-28
  2. Department: Industrial and Management Systems Engineering
  3. College: EN
  4. Budget Account Number: 210300
  5. Contact Person: Alex Savachkin
  6. Phone: 8139745577
  7. Email:
  8. Prefix: ESI
  9. Number: 6346
  10. Full Title: Stochastic Decision Models II
  11. Credit Hours: 3
  12. Section Type: C - Class Lecture (Primarily)
  13. Is the course title variable?: N
  14. Is a permit required for registration?: N
  15. Are the credit hours variable?: N
  16. Is this course repeatable?: Y
  17. If repeatable, how many times?: 3
  18. Abbreviated Title (30 characters maximum): Stochastic Decision Models II
  19. Course Online?: C - Face-to-face (0% online)
  20. Percentage Online: 0
  21. Grading Option: R - Regular
  22. Prerequisites: ESI 6213 Stochastic Decision Models I
  23. Corequisites: none
  24. Course Description: Introduction to modern decision and risk analysis and utility theory. It focuses on themathematical foundations underlying the quantification and management of risk to support dynamic decision making under uncertainty.

  25. Please briefly explain why it is necessary and/or desirable to add this course: Replacing Selected Topics with Permanent number; already listed in program
  26. 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.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Yes, 1 time
  28. 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.
  29. 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.

  30. 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.

  31. 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))

  32. Textbooks: Dynamic probabilistic systems, R. Howard, 2007.
  33. 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.

  34. Student Expectations/Requirements and Grading Policy: Grading policy. Three exams will be given each worth 33.33% of the final grade.
  35. Assignments, Exams and Tests: Two midterms and one final exam. Each is 1/3 of the grade.
  36. 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. Its 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.

  37. 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.

  38. Program This Course Supports: MSIE and PhD in Industrial Engineering
  39. 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