GRADUATE SCHOOL

M.SC. In Industrial Engineering (With 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
X
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

To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems.

X
2

To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment.

X
3

To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality.

X
4

To be able to conduct basic research and write and publish articles in related conferences and journals.

X
5

To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results.

X
6

To be able to manage decision-making processes in industrial systems.

X
7

To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary.

X
8

To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering.

X
9

To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria.

X
10

To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.

X

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

 


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