COURSE SYLLABUS

LARGE SCALE OPTIMIZATION

1	Course Title:	LARGE SCALE OPTIMIZATION
2	Course Code:	END6151
3	Type of Course:	Optional
4	Level of Course:	Third Cycle
5	Year of Study:	2
6	Semester:	3
7	ECTS Credits Allocated:	7,5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	END5101 Mathematical Programming
12	Recommended optional programme components:	COURSE CONTENT: This course will cover algorithms and techniques for large-scale optimization with an emphasis on mixed-integer programming and implementation issues. The following topics will be covered: Search algorithms (branch-and-bound, branch-and-cut, branch-and-price, constraint propagation), quality of relaxation, infeasibility analysis, decomposition and relaxation methods (Lagrangean, Dantzig-Wolfe, Benders), dynamic column and row generation, parallel computing (Moore’s law, Amdahl’s law, threads, race conditions, synchronization) and constraint programming.
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Doç. Dr. BURCU ÇAĞLAR GENÇOSMAN
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	e-posta: burcucaglar@uludag.edu.tr, Telefon: + 90 (224) 294 20 89 Adres: Bursa Uludağ Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Görükle Kampüsü, 16059 Nilüfer, Bursa
18	Website:
19	Objective of the Course:	This course aims to provide students with the ability to model and solve combinatorial optimization problems with the techniques that will be taught in the course.
20	Contribution of the Course to Professional Development	It's been planned to contribute to professional development by analyzing real-life problems with scientific methods and providing solutions.

Learning Outcomes:

1	Learning search and relaxation algorithms used in large-scale combinatorial problems.;
2	Representation of a combinatorial problem with the basic constraint expressions of the constraint solver and creating a constraint programming model.;
3	Modeling real-world integer problems with constraint programming.;

22	Course Content:

Week	Theoretical	Practical
1	Review on Linear Optimization
2	Effective modeling in integer programming, search algorithms
3	Lagrangian Relaxation and Duality
4	Lagrangian Relaxation and Duality
5	Dantzig-Wolfe Decomposition
6	Column generation
7	Benders Decomposition and Delayed Constraint Generation
8	Logic-Based Benders decomposition
9	Review on Nonlinear Optimization
10	Cutting-plane and dynamic constraint generation
11	Parallel computing
12	Constraint programming
13	Constraint programming
14	Project Presentations

23	Textbooks, References and/or Other Materials:	“Optimization Theory for Large Systems” Leon S. Lasdon, Dover edition, 2002. “Linear Programming and Network Flows” Mokhtar S. Bazaraa, John J. Jarvis, Hanif D. Sherali, 4th edition, 2009. “Integer and Combinatorial Optimization” Laurence A. Wolsey, George L. Nemhauser, William, 1999. “IBM ILOG CPLEX Optimization Studio V22.1 documentation”, IBM, 2024
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	20
Quiz	0	0
Homeworks, Performances	2	20
Final Exam	1	60
Total	4	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Measurement and evaluation is carried out according to the priciples of Bursa uludag University Associate and Postgraduate Education Regulation.
Information

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	13	5	65
Homeworks, Performances	2	36	36
Projects	1	81	81
Field Studies	0	0	0
Midtermexams	1	2	2
Others	0	0	0
Final Exams	1	2	2
Total WorkLoad			228
Total workload/ 30 hr			7,6
ECTS Credit of the Course			7,5

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

	PQ1	PQ2	PQ3	PQ4	PQ5	PQ6	PQ7	PQ8	PQ9	PQ10	PQ11	PQ12
LO1	0	0	5	5	2	2	2	2	1	1	0	0
LO2	0	0	5	5	4	4	4	4	1	1	1	1
LO3	1	1	5	5	5	5	5	5	1	1	4	4

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High