COURSE UNIT TITLE

: FUNDAMENTALS OF OPTIMIZATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IND 5036 FUNDAMENTALS OF 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

ASSOCIATE PROFESSOR ŞENER AKPINAR

Offered to

INDUSTRIAL ENGINEERING - NON THESIS
Industrial Engineering - Thesis (Evening Program)
INDUSTRIAL ENGINEERING
INDUSTRIAL ENGINEERING
INDUSTRIAL ENGINEERING - NON THESIS (EVENING PROGRAM)

Course Objective

This course introduces students to Optimization Theory and its use in Operations Research, Industrial Engineering and allied disciplines. The course covers the theory and practical techniques needed to solve nonlinear programming problems. Emphasis will be on formulating optimization problems, understanding the methods available to address them, and solving them using appropriate means.

Learning Outcomes of the Course Unit

1   To make the students aware of the nonlinear programming problems in operations research and industrial engineering
2   To devolop the students' skills in solving unconstrained nonlinear programming problems
3   To devolop the students' skills in solving constrained nonlinear programming problems
4   To enable the students write their specific solution algorithms in a programming language in order to solve their nonlinear models

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Improving Search : Local and Global Optima, Search with Improving and Feasible Directions, Algebraic Conditions for Improving and Feasible Directions
2 Unimodal and Convex Model Forms Tractable for Improving Search, Searching and Starting Feasible Solutions
3 Unconstrained Nonlinear Programming: Unconstrained Nonlinear Programming Models, One-Dimensional Search
4 Unconstrained Nonlinear Programming: Derivatives, Taylor Series, and Conditions for Local Optima
5 Convex/Concave Functions and Global Optimality, Gradient Search
6 Newton's Search, Quasi-Newton Methods and BFGS Search
7 Constrained Nonlinear Programming: Constrained Nonlinear Programming Models, Convex, Separable, Quadratic and Posynomial Geometric Programming Special NLP Forms, Lagrange Multiplier Methods
8 Karush-Kuhn-Tucker Optimality Conditions
9 Midterm
10 Penalty and Barrier Methods
11 Reduced Gradient Algorithms
12 Quadratic Programming Methods
13 Project Presentations
14 Project Presentations

Recomended or Required Reading

Ronald L. Rardin, Optimization in Operations Research, Prentice Hall, 1998
S.G. Nash, A. Sofer, Linear and Nonlinear Programming, McGrawHill, 1996
W.L. Winston, Operations Research, Applications and Algorithms, Thomson Edu, 2004

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, student presentations are organized and they are presented at the scheduled times for all the students.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 CAS CASE STUDY
2 PRJ PROJECT
3 PRJ PROJECT
4 FCG FINAL COURSE GRADE CAS * 0.25 + PRJ * 0.25 + PRJ * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) CAS * 0.25 + PRJ * 0.25 + RST * 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc.Prof.Dr. Şeyda Topaloğlu
e-mail: seyda.topaloglu@deu.edu.tr
Tel: 301 7611

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 11 3 33
Preparation for midterm exam 1 30 30
Preparations before/after weekly lectures 11 1 11
Preparation for homeworks 3 10 30
Preparation for final exam 1 40 40
Preparation for term paper 1 30 30
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 178

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.12311
LO.24211333
LO.3134222
LO.4421113

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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