Preview Workflow

The CIM Courses system will be down temporarily undergoing routine maintenance.

Viewing: DNSC 8394 : Stochastic Programming

Last approved: Tue, 18 Jul 2017 08:05:03 GMT

Last edit: Wed, 12 Jul 2017 14:43:48 GMT

Catalog Pages referencing this course
School of Business
Decision Sciences (DNSC)
DNSC
8394
Stochastic Programming
Stochastic Programming
Fall 2017
3
Course Type
Lecture
Default Grading Method
Letter Grade

No
No

Corequisites

10
Lejeune
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


The intersection of probability theory and statistics with mathematical programming for modeling optimization problems that involve uncertainty. Basic knowledge of linear programing, elementary analysis and probability. Emphasis on algorithmic methods to solve stochastic programming instances.
Students will:

• Develop the capacity to model stochastic systems and decision processes and to formulate the corresponding optimization programs.
• Learn the concepts and the basic theory of the stochastic programming discipline.
• Understand the structure of stochastic models and programs.
• Get acquainted with algorithmic techniques used in stochastic programming.
• Learn stochastic computational tools, solvers and algebraic modeling languages. The AMPL modeling language will be extensively used to model and solve different types of stochastic optimization problems.
• Incorporate stochastic modeling and programming into your research.

Course Attribute


Key: 10939