Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
3	IE532	STOCHASTIC PROGRAMMING	3+0+0	3	7,5	13.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	INDUSTRIAL ENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	This course deals with optimization under data uncertainty. It is intended for the students to give a detailed introduction about stochastic programming with modelling, theoretical results and computational methods
Course Content	Two-stage stochastic linear programs, Chance-constrained stochastic programs, L-shaped method with improved stages, Monte-Carlo methods
Course Methods and Techniques	The course will be taught through theoretical lectures, sample problem solving, class discussions and practical exercises. In addition, group work and interactive learning techniques will be used to increase student participation. Homework assignments and projects will be given to reinforce the topics.
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Associate Prof.Dr. Ramazan Ünlü
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Birge, John R., and Louveaux, François. Introduction to Stochastic Programming. Springer, 2011 Shaprio, Alexander, Dentcheva, Darinka, and Ruszczynski, Adrej. Lectures on Stochastic Programming Modeling and Theory. SIAM and MPS, 2009. Kall, Peter, and Mayer, János. Stochastic Linear Programming: Models, Theory, and Computation. Springer, 2011.
Course Notes	It will be shared weekly from the canvas.
Documents	-
Assignments	Canvas üzerinden paylaşılacaktır.
Exams	Canvas üzerinden paylaşılacaktır.

Course Category

Mathematics and Basic Sciences	%50
Engineering	%25
Field	%25

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Araştırma Ödevi	1	10	10
Grup Sunumu	1	2	2
Grup Projesi	1	15	15
Ev Ödevi	5	5	25
Sunum için Hazırlık	1	10	10
Teslim İçin Hazırlık	1	10	10
Kısa Sınav	5	3	15
Rapor	1	10	10
Araştırma	16	1	16
Kişisel Çalışma	16	4	64
Yüz Yüze Ders	16	3	48
Final Sınavı	1	3	3
Total Work Load			Number of ECTS Credits 7,5 228

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Applies the basic modeling methods of stochastic programming, differantiates the differences between them,
2	Formulates the deterministic equivalent of a stochastic model,
3	Applies the methods used for the complete solution,
4	Applies the methods used for predictive solutions,
5	Lists the methods used to solve integer models.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Modeling, deterministic equivalent formulation
2	Two-stage stochastic linear programming
3	Chance-constrained stochastic programs,
4	Multi-stage stochastic linear programs
5	Integer stochastic programs
6	Expected value of perfect information, Value of stochastic solution.
7	L-Shaped Algorithm
8	Advanced techniques, regularized decomposition method, trickling down, bundle-trust region method
9	Midterm, Progress report and presentation
10	Solution methods for multi-stage stochastic programs
11	Solution methods for integer stochastic programs
12	Upper and lower bounds, Monte-Carlo, Edmundson-Madansky inequalities
13	Monte-Carlo Methods
14	Multistage Stochastic Programs
15	Project Final Presentation
16	Final Exam

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77988&lang=en