GRADUATE SCHOOL

M.SC. in Computer Engineering (Without Thesis)

IE 535 | Course Introduction and Application Information

Course Name
Advanced Network Optimization
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 535
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Second Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator -
Course Lecturer(s) -
Assistant(s) -
Course Objectives
Learning Outcomes The students who succeeded in this course;
  • Upon successful completion of this course, the students will:
  • Acquire the skills to identify and formulate a wide variety of engineering applications as network flow problems.
  • Become familiar with well studied network flow problems like shortest path, minimum spanning tree and maximal flow.
  • Know various techniques to solve network optimization problems.
  • Able to apply dynamic programing for dynamic decision problems.
Course Description Topics of this course include the shortest path problem, the maximum flow problem, the minimum cost flow problem, the minimum spanning tree problem and dynamic programming.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Notation and definitions. Paths, trees and cycles Textbook Chapter 2
2 Shortest paths Textbook Chapter 4
3 Shortest paths Textbook Chapter 5
4 Maximum flows Textbook Chapter 6
5 Maximum flows Textbook Chapter 7 and 8
6 Minimum spanning trees Textbook Chapter 13
7 Minimum cost flows Textbook Chapter 9 and 10
8 Minimum cost flows Textbook Chapter 11
9 Multi-commodity flow problem Textbook Chapter 17
10 Midterm
11 Introduction to Dynamic Programming Denardo
12 Deterministic Dynamic programming Denardo
13 Deterministic Dynamic programming Denardo
14 Stochastic Dynamic Programming Denardo
15 Stochastic Dynamic Programming Denardo
16 Stochastic Dynamic Programming Denardo

 

Course Notes/Textbooks Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin, Network Flows: Theory, Algorithms, and Applications, Prentice Hall Instructor notes and lecture slides.
Suggested Readings/Materials Eric V. Denardo, Dynamic Programming Models and Applications, Prentice Hall

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
4
40
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
30
Final Exam
1
30
Total

Weighting of Semester Activities on the Final Grade
60
Weighting of End-of-Semester Activities on the Final Grade
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
15
6
90
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
4
10
40
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
20
20
Final Exam
1
27
27
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information. X
2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations. X
3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines. X
4 Is informed about new and upcoming applications in the field and learns them whenever necessary. X
5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions. X
6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs. X
7 Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process. X
8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility. X
9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. X
10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. X
11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications. X
12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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