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Code IE 512
Term 201702
Title Graph Theory and Network Flows
Faculty Faculty of Engineering and Natural Sciences
Subject Industrial Engineering(IE)
SU Credit 3
ECTS Credit 10.00
Instructor(s) Guvenc Sahin,
Detailed Syllabus
Language of Instruction English
Level of Course Doctoral
Type of Course Click here to view.
(only for SU students)
Mode of Delivery Formal lecture

Theory and applications of graphs and networks; properties of graphs; Hamiltonian and Eulerian walk problems; Travelling salesman problem and variants; design and analysis of shortest path, maximum flow and minimum cost network flow algorithms; matching and assignment; network simplex algorithm.


In this course, students will be exposed to various network flow problems. Mainly, applications, basic algorithms and strongly polynomial algorithms will be presented for each type of problem. This course may be interesting for industrial engineering, computer science and telecommunications engineering graduate students depending on their research interests.

Learning Outcome

Upon completion of this course, students should be able to:
Describe a problem by using graphs and/or networks as an abstract model.
Distinguish the main aspects of different network representation approaches.
Develop a network representation of the problem by using the abstract graph/network based model.
Among shortest path, maximum flow and minimum cost flow problems, identify the type of network flow problem to be solved for the network representation of the problem.
Analyze the computational complexity of the algorithms for network flow problems.
Compare the use of different data structures with respect to their effect on the complexity of the algorithms, and observe the theoretical and empirical differences among the complexities.
Associate graph-theoretic problems with their combinatorial structures.

Programme Outcomes
Common Outcomes For All Programs
1 Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications 4
2 Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas. 5
3 Evaluate and use new information within the field in a systematic approach. 3
4 Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field; research, conceive, design, adapt and implement an original subject. 1
5 Critical analysis, synthesis and evaluation of new and complex ideas. 1
6 Gain advanced level skills in the use of research methods in the field of study. 4
7 Contribute the progression in the field by producing an innovative idea, skill, design and/or practice or by adapting an already known idea, skill, design, and/or practice to a different field independently. 1
8 Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals. 1
9 Demonstrate leadership in contexts requiring innovative and interdisciplinary problem solving. 1
10 Develop new ideas and methods in the field by using high level mental processes such as creative and critical thinking, problem solving and decision making. 5
11 Investigate and improve social connections and their conducting norms and manage the actions to change them when necessary. 1
12 Defend original views when exchanging ideas in the field with professionals and communicate effectively by showing competence in the field. 1
13 Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level. 1
14 Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements. 1
15 Demonstrate functional interaction by using strategic decision making processes in solving problems encountered in the field. 4
16 Contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values. 1
Common Outcomes For All Programs
1 Develop the ability to use critical, analytical, and reflective thinking and reasoning 5
2 Reflect on social and ethical responsibilities in his/her professional life. 1
3 Gain experience and confidence in the dissemination of project/research outputs 1
4 Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments. 1
5 Communicate effectively by oral, written, graphical and technological means and have competency in English. 3
6 Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating. 2
Common Outcomes ForFaculty of Eng. & Natural Sci.
1 Design and model engineering systems and processes and solve engineering problems with an innovative approach. 3
2 Establish experimental setups, conduct experiments and/or simulations. 2
3 Analytically acquire and interpret data. 5
Physics (non-thesis) Program Outcomes Core Electives
1 Employ mathematical methods to solve physical problems and understand relevant numerical techniques. 4
2 Conduct basic experiments or simulations. 1
3 Analytically acquire and interpret data. 3
4 Establish thorough understanding of the fundamental principles of physics. 1
Industrial Engineering (with thesis) Program Outcomes Core Electives
1 Establish a strong theoretical background in several of a broad range of subjects related to the discipline, such as manufacturing processes, service systems design and operation, production planning and control, modeling and optimization, stochastics, statistics. 5
2 Develop novel modeling and / or analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy, also using an interdisciplinary approach whenever appropriate. 4
3 Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 4
4 Acquire skills to independently explore and tackle problems related to the discipline that were not encountered previously. Develop appropriate modeling, solution, implementation strategies, and assess the quality of the outcome. 4
Electronics Engineering (with thesis) Program Outcomes Core Electives
1 Use advanced Math (including probability and/or statistics), advanced sciences, advanced computer and programming, and advanced Electronics engineering knowledge to design and analyze complex electronics circuits, instruments, software and electronic systems with hardware/software. 1
2 Analyze and design advanced communication networks and systems, advanced signal processing algorithms or software using advanced knowledge on diff. equations, linear algebra, complex variables and discrete math. 2
Molecular Biology, Genetics and Bioengineering (with thesis) Program Outcomes Area Electives
1 Apply knowledge of key concepts in biology, with an emphasis on molecular genetics, biochemistry and molecular and cell biology. 1
2 Display an awareness of the contemporary biological issues in relation with other scientific areas. 1
3 Demonstrate hands-on experience in a wide range of biological experimental techniques. 1
Energy Technologies and Management (with thesis) Program Outcomes Area Electives
1 Assess and identify developments, strategies, opportunities and problems in energy security and energy technologies. 1
2 Define and solve technical, economic and administrative problems in energy businesses. 1
3 Establish knowledge and understanding of energy security, energy technologies, energy markets and strategic planning in energy enterprises. 1
4 Demonstrate an awareness of environmental concerns and their importance in developing engineering solutions and new technologies. 1
5 Acquire a series of social and technical proficiencies for project management and leadership skills. 1
Materials Science and Engineering (with thesis) Program Outcomes Area Electives
1 Apply a broad knowledge of structure & microstructure of all classes of materials, and the ability to use this knowledge to determine the material properties. 1
2 Apply a broad understanding of the relationships between material properties, performance and processing. 1
3 Apply a broad understanding of thermodynamics, kinetics, transport phenomena, phase transformations and materials aspects of advanced technology. 1
4 Demonstrate hands-on experience using a wide range of materials characterization techniques. 1
5 Demonstrate the use of results from interpreted data to improve the quality of research, a product, or a product in materials science and engineering. 2
Computer Science and Engineering (with thesis) Program Outcomes Area Electives
1 Apply knowledge of mathematics, science, and engineering in computer science and engineering related problems. 4
2 Display knowledge of contemporary issues in computer science and engineering and apply to a particular problem. 4
3 Demonstrate the use of results from interpreted data to improve the quality of research or a product in computer science and engineering. 5
Mechatronics Engineering (with thesis) Program Outcomes Area Electives
1 Apply software, modeling, instrumentation, and experimental techniques and their combinations in the design and integration of electrical, electronic, control and mechanical systems. 1
2 Interact with researchers from different disciplines to exchange ideas and identify areas of research collaboration to advance the frontiers of present knowledge and technology; determine relevant solution approaches and apply them by preparing a research strategy. 1
3 Take part in ambitious and highly challenging research to generate value for both the industry and society. 1
Assessment Methods and Criteria
  Percentage (%)
Final 40
Midterm 30
Exam 10
Individual Project 20
Recommended or Required Reading

Ahuja, Ravindra K., Thomas L. Magnanti, and James B. Orlin. Network Flows: Theory, Algorithms and Applications, Prentice Hall, 1993.


Aldous, Joan M., and Robin J. Wilson. Graphs and Applications: An Introductory Approach. Springer Science & Business Media, 2003.
Balakrishnan V. Network Optimization, Chapman and Hall, 1995.
Bertsekas, Dimitri P. Network Optimization: Continuous and Discrete Methods, Athena Scientific, 1998.
Bertsekas, Dimitri P. Linear Network Optimization: Algorithms and Codes, MIT Press, 1991.
Winston, Wayne L., and Jeffrey B. Goldberg. Operations Research: Applications and Algorithms, Duxbury Press, 2004.