COURSE UNIT TITLE

: STOCHASTIC PROGRAMMING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EKO 6122 STOCHASTIC PROGRAMMING ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR MURAT TANIK

Offered to

Econometrics

Course Objective

The aim of the course is to introduce students to methods of optimal decision making in situations of uncertainty data. Stochastic programming operations research area is developing rapidly with contributions from many different disciplines such as mathematics and probability. Stochastic programming operations research area is developing rapidly with contributions from many different disciplines such as mathematics and probability.

Learning Outcomes of the Course Unit

1   To be able to identify areas of application of stochastic problems.
2   To be able to use optimization techniques in decision making problems.
3   To be able to formulate problems with data uncertainty, be able to solve and analyze.
4   To be able to solve stochastic optimization problems with programming models.
5   To be able to use software to perform the solution of the stochastic programming model.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic concepts of modeling
2 Deterministic solutions of stochastic programming problems
3 Two-stage stochastic linear programming (SP)
4 Stochastic constraints, stochastic integer programming, multistage stochastic programming problems
5 Limits on stochastic programming: Expected Value of Perfect Information, Value of Stochastic Solution
6 Article studies
7 Midterm Exam
8 Limits on stochastic programming: Wait-and-See bounds, Jensen's Inequality, Edmunso the-Madansky Inequality
9 Calculation Methods: The L-Shaped method. Methods Multicut. Bunching and other algorithmic Techniques.
10 Calculation methods: stabilizing the L-Shaped method. Regularized ecompositio the Progressive Hedging.
11 Article investigations
12 Sampling methods for large-scale problems: stochastic decomposition
13 Sampling methods for large-scale problems: variance reduction techniques
14 Applications and presentations

Recomended or Required Reading

John R. Birge and Fran cois Louveaux. Introduction to Stochastic Programming (SpringerVerlag, 1997).

Planned Learning Activities and Teaching Methods

Lecture Method, Question-Answer Method, Method Discussion and problem solving Metodu- Applications

Assessment Methods

Successful / Unsuccessful


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

Students should come to class for success.
Students have to make the required weekly and semester homework and projects.
It is the student's responsibility to follow the weekly and semester homework and projects.

Contact Details for the Lecturer(s)

Assoc. Prof. Dr. Mehmet AKSARAYLI
mehmet.aksarayli@deu.edu.tr
0-232-301 02 81

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 4 52
Preparation for midterm exam 1 35 35
Preparation for final exam 1 35 35
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 167

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.1222222222
LO.2222222222
LO.3222222222
LO.4222222222
LO.5222222222

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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