COURSE UNIT TITLE

: NETWORK ANALYSIS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5084 NETWORK ANALYSIS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, finance as well as a number of other domains. This objective of this course is to survey some of the applications of network flows and focus on key special cases of network flow problems.

Learning Outcomes of the Course Unit

1   Defining basic concepts of network flows
2   Analyze systems in which there is a flow of products and/or services
3   Developing efficient algorithms for performing the analysis of network optimization problems
4   Modeling the systems mathematically which desires to optimize different objectives
5   Analyzing different algorithms which is developed for different objectives
6   Developing skills on project planning
7   Interpreting consequences obtained as a result of the different models
8   Developing solutions and suggestions to optimize the network

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Network Models
2 Graph Search Algorithms
3 Transformations and Flow Decomposition
4 Shortest Paths: Label Setting Algorithms, Label Correcting Algorithms, Preparing Individual Assignments
5 Basic Algorithms for The Maximum Flow Problem
6 Combinatorial Applications of Maximum Flows
7 The Global Min Cut Algorithm, Minimum Cost Flows: Basic Algorithms
8 Mid-term exam
9 The Successive Shortest Path Algorithm
10 The Network Simplex Algorithm, Preparing Individual Assignments
11 Minimum Spanning Trees
12 Review of Linear Programming, Generalized Flows, Preparing Presentations
13 Multicommodity Flows, Preparing Presentations
14 Applications of netwok flows

Recomended or Required Reading

Textbook(s):
Ahuja R., Magnanti T, Orlin J., Network Flows, Theory, Algorithms, and Applications, Prentice Hall, 1993.
Supplementary Book(s):

Planned Learning Activities and Teaching Methods

Lecture and problem solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRS PRESENTATION
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + FIN * 0.40
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams, homeworks and presentations.

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy.

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: cengiz.celikoglu@deu.edu.tr
Tel: 0232 301 85 50

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparing assignments 2 20 40
Preparing presentations 2 10 20
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Preparations before/after weekly lectures 14 4 56
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 197

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555
LO.6555
LO.7555
LO.8555