Network Models (IE510) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Network Models IE510 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Technical Elective Courses
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives In this course, the students will be learning fundamental concepts of network models to be able to apply for their practical problems.
Course Learning Outcomes The students who succeeded in this course;
  • Students will have an understanding of the network models and related concepts.
  • Students will develop an insight about the role of network models for various engineering disciplines.
  • Students will be able to formulate and solve real life processes and problems using network models.
  • Students will have an understanding of project scheduling and its applications in industrial engineering and management.
Course Content Review, flow problems, special purpose algorithms and advanced computational techniques for transportation and assignment problems, maximum flow problems, shortest paths, minimum cost flows, network simplex method, multicommodity flow problems, generalized networks, network model applications for production planning problems, travelling salesperson

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to network flow problems
2 Countinued
3 Special purpose algorithms and advanced computational techniques for transportation and assignment problems.
4 Theory, algorithms, and applications for maximum flow problems.
5 The minimum cost flow problems and applications to production planning
6 Continued
7 Continued
8 The Network simplex method.
9 Continued
10 Midterm
11 Project Scheduling: CPM/PERT, time cost trade-off, time resource trade-off problems
12 Resource constrained project scheduling, combinatorial methods and heuristic
13 The travelling salesperson and similar combinatorial problems
14 Countinued
15 Final Examination Period
16 Final Examination Period

Sources

Course Book 1. R.K. Ahuja, T.L. Magnanti, J.B. Orlin, Network Flows: Theory, Algorithms and Applications, Prentice Hall, 1993.
Other Sources 2. J.D. McCabe, Network Analysis, Architecture and Design, 3rd edition, Morgan Kaufmann, 2007.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 3 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses
Major Area Courses
Supportive Courses X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to apply the acquired knowledge in mathematics, science and engineering X
2 Ability to identify, formulate and solve complex engineering problems X
3 Ability to accomplish the integration of systems X
4 Ability to design, develop, implement and improve complex systems, components, or processes X
5 Ability to select/develop and use suitable modern engineering techniques and tools X
6 Ability to design/conduct experiments and collect/analyze/interpret data
7 Ability to function independently and in teams X
8 Ability to make use of oral and written communication skills effectively X
9 Ability to recognize the need for and engage in life-long learning X
10 Ability to understand and exercise professional and ethical responsibility X
11 Ability to understand the impact of engineering solutions X
12 Ability to have knowledge of contemporary issues X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration
Project 1 4 4
Report
Homework Assignments 4 4 16
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 16 16
Prepration of Final Exams/Final Jury 1 25 25
Total Workload 125