COURSE UNIT TITLE

: DISCRETE OPTIMIZATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5083 DISCRETE OPTIMIZATION ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Statistics
Statistics
STATISTICS

Course Objective

The objective is to introduce the discrete and combinatorial optimization methods and to study integer programming techniques and applications.

Learning Outcomes of the Course Unit

1   Defining basic concepts of discrete optimization
2   Using mathematical techniques to express for discrete and combinatorial optimization problems
3   Analyzing integer programming problems
4   Analyzing network flow problems
5   Analyzing shortest path problems
6   Interpreting and presenthe solutions of discrete optimization problems
7   Gaining both theoretical and practical skills applying various optimization models to different areas

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Mathematical background for discrete and combinatorial optimization
2 Mathematical background for discrete and combinatorial optimization
3 Formulating Integer Programming problems, Preparing Individual Assignments
4 Formulating Integer Programming problems, Preparing Presentations
5 Solving Integer Programming Problems: Branch and bound method
6 Solving Integer Programming Problems: Branch and bound method
7 Solving Integer Programming Problems: Cutting Planes
8 Mid-term exam
9 Applications of Network Models, Preparing Individual Assignments
10 Minimum spending tree
11 Network Flow Problems
12 Shortest Path Problems
13 Heuristics, Preparing Presentations
14 Minimum cost flow

Recomended or Required Reading

Textbook(s):
Rardin, R., Optimization in Operations Research, Prentice Hall, 1998.
Nemhauser, G.And Wolsey, L., Integer and Combinatorial Optimization, Wiley, 1988.
Ahuja, R.K., Magnanti, T.L., and Orlin, J.B., Network Flows: Theory, Algorithms, and Applications, Prentice Hall, 1993.
Supplementary Book(s):

Planned Learning Activities and Teaching Methods

Lecture, homework 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


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams, presentations and homeworks.

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
Preparation for midterm exam 1 10 10
Preparation for final exam 1 15 15
Preparing presentations 2 10 20
Preparations before/after weekly lectures 14 4 56
Preparing assignments 2 15 30
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 177

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

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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