ISE 6290
Transcript Abbreviation:
Stochastic Optimiz
Course Description:
Examines the modeling, theory, and solution algorithms for stochastic optimization problems.
Course Levels:
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 5200, and Stat 3470 or ISE 7300; or permission of instructor.
Electronically Enforced:
No
Exclusions
(N/A)
Course Goals / Objectives:
Learn about different modeling techniques in stochastic optimization
Structural properties of recourse problems
Decomposition-based solution algorithms
Apply optimization software to solve stochastic optimization problems
Structural properties of recourse problems
Decomposition-based solution algorithms
Apply optimization software to solve stochastic optimization problems
Be able to code and implement decomposition-based algorithms
Monte Carlo Sampling-Based Methods
Risk Models, their properties and applications
Monte Carlo Sampling-Based Methods
Risk Models, their properties and applications
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Stochastic Optimization Modeling | 5.0 | 0.0 | 0.0 | 0 |
| Structural Properties of the Recourse Model | 6.0 | 0.0 | 0.0 | 0 |
| Decomposition Algorithms: L-shaped and Regularized L-Shaped | 6.0 | 0.0 | 0.0 | 0 |
| Decomposition Algorithms: Nested Benders and Progressive Hedging | 6.0 | 0.0 | 0.0 | 0 |
| Bounding methods | 5.0 | 0.0 | 0.0 | 0 |
| Monte Carlo Sampling-based Methods | 7.0 | 0.0 | 0.0 | 0 |
| Risk Models and Properties | 7.0 | 0.0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Problem Sets | 33% |
| Exam | 33% |
| Project | 34% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No required textbook. Professor Lecture Notes and Suggested Texts: | Shapiro, A., Dentcheva, D. and A. Ruszczynski, Lectures on Stochastic Programming: Modeling and Theory. Society of Industrial and Applied Mathematics (SIAM), Philadelphia, 2009. | |
| No required textbook. Professor Lecture Notes and Suggested Texts: | Birge, J.R. and F. Louveaux, Introduction to Stochastic Programming. Springer-Verlag, NewYork | |
| No required textbook. Professor Lecture Notes and Suggested Texts: | Kall, P. and S.W. Wallace, Stochastic Programming, John Wiley and Sons, 1994. This book is available online free of charge. A copy can be found at: http://www.lancs-initiative.ac.uk/page/153/Resources.htm |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes
(N/A)
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)