COURSE UNIT TITLE

: DETERMINISTIC OPTIMIZATION METHODS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5009 DETERMINISTIC OPTIMIZATION METHODS 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

PROFESSOR DOCTOR UMAY ZEYNEP UZUNOĞLU KOÇER

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

The aim of this course is to focus on decision problems encountered in business. To this aim, the topics including modeling industrial problems by using linear or non-linear models and methods for finding the optimum solution for these models will be covered. For finding the optimal solution of the models will be studied by using computer-aided techniques such as Matlab, Excel, R, Python.

Learning Outcomes of the Course Unit

1   Define the basic concepts in optimization
2   Construct both linear and non-linear mathematical models for industrial problem, depending on its structure
3   Solve an optimization problem with a suitable method
4   Solve an optimization problem by using a computer programming language
5   Interpret the numerical results
6   Make plans to use the existing resources optimum

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic concepts on optimization
2 Mathematical modeling in data analysis
3 Constructing mathematical models: Linear models
4 Solving the linear models and heuristic approximations on big data; interpreting the results
5 Solving the linear integer models and interpreting the reults Integration of machine learning algorithms to branch-and-bound algorithm
6 Midterm
7 Constructing mathematical models: Non-linear models
8 Optimization of unconstrained non-linear models
9 The method of steepest ascent and golden section search
10 Optimization of constrained non-linear models: Lagrange multiplier
11 Optimization of constrained non-linear models: Kuhn-Tucker conditions
12 Dynamic programming and dynamic optimization with big data
13 Computer-aided case studies: Solving large scale problems by coding
14 Computer-aided case studies: Meta-heuristics approximations in the analysis of big data.

Recomended or Required Reading

Main Textbook(s):
1. R.L. Rardin, Optimization in Operations Research, Prentice Hall, USA
Supplementary Book(s):
2. W.L. Winston, Introduction to Operations Research, Applications and Applications, 3rd edition,
3. Duxbury Press, 1994.
4. H. Taha, Operations Research, McGraw Hill, 7th edition, 2003.
5. F.S. Hillier, G.J. Lieberman, Introduction to Operations Research, McGraw Hill, 1995.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 +ASG * 0.10 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 +ASG * 0.10 +RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

DEU, Faculty of Science, Department of Statistics
e-mail: umay.uzunoglu@deu.edu.tr
phone: +90 232 301 85 60

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 20 20
Preparation for final exam 1 30 30
Preparing assignments 1 30 30
Preparing presentations 1 30 30
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 194

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.154
LO.2455
LO.355
LO.44555
LO.525
LO.6