NMAK15004U CANCELLED - Advanced Operations Research: Stochastic Programming
MSc programme in Mathematics-Economy
This course is about optimization under uncertainty by means of stochastic programming. Special emphasis is placed on different problem formulations and selected scenario generation methods as well as to understand specific properties of stochastic programming problems and how to exploit these properties in various solution methods. Furthermore, the students of this course will independently handle more practical problems by stochastic programming.
A. Stochastic programming problems:
- A1. Formulation of two-stage and multi-stage recourse problems, simple recourse, linear and integer problems, chance constrained problems.
- A2. Examples.
- A3. Implementation and solution of problems in GAMS or another suitable software program.
- A4. Analysis of the solution.
B. Scenario generation:
- B1. Moment matching.
- B2. Sampling.
- B3. Scenario tree construction and reduction.
- B4. The quality of scenario generation methods.
C. Properties of stochastic programming problems:
- C1. The value of stochastic programming: EVPI and EEV.
- C2. Structural properties: Continuity and convexity.
D. Solution methods:
- D1. L-shaped decomposition.
- D2. Integer L-shaped decomposition
- D3. Dual decomposition.
E. Practical aspects and applications:
- E1. Case: Energy planning.
- E2. Case: Finance or Transportation.
- E3. Implementation of a specific problem in GAMS.
- E4. Implementation of a solution method in GAMS.
Knowledge:
- Formulations of stochastic programming problems.
- Scenario generation methods.
- Properties of stochastic programming problems.
- Solution methods.
Skills:
- Formulate two-stage and multi-stage recourse problems
- Implement and solve a specific stochastic programming problem in GAMS or another suitable software program
- Apply selected methods to describe the uncertainty of the problem (so-called scenario generation methods)
- Apply the solution methods presented in the course
- Implement a specific solution method in GAMS (in a simplified fashion).
- Understand and reproduce the proofs presented in the course
Compentences:
- Work out simple proofs using the same techniques as in the course
- Discuss the challenges of solving SP problems
- Explain how to exploit the properties of a given class of SP problems in the design of a solution method
- Adapt a solution method to a given class of SP problems, and make small changes to and extensions of the method
- Evaluate the quality of scenario trees
- Discuss the challenges of modeling and solving practical problems
- Formulate, implement and solve a practical problem and justify the choice of model formulation, scenario generation method and solution method
Recommended but not required: One or more of the following courses: Modelling and GAMS, Optimization and Convexity or Operations Research 2 (OR2)
- Category
- Hours
- Exam
- 50
- Lectures
- 28
- Preparation
- 70
- Project work
- 44
- Theory exercises
- 14
- Total
- 206
As
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- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 30 min30 minutes oral examination with 30 minutes preparation time.
- Exam registration requirements
Approval of two project reports is a prerequisite for enrolling for examination (failed project reports can be resubmitted)
- Aid
- Written aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
Same as ordinary exam. If the required project reports were not approved before the ordinary exam they must be resubmittet no later than two weeks before the beginning of the re-exam week
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK15004U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 3
- Schedule
- B
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Giovanni Pantuso (2-767f4f7c7083773d7a843d737a)