COURSE UNIT TITLE

: APPLIED OPTIMIZATION

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DBA 6363 APPLIED OPTIMIZATION ELECTIVE 3 0 0 8

Offered By

Business Administration (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR SABRI ERDEM

Offered to

Business Administration (English)

Course Objective

Aim of this course is to provide students with the theory, computational methods, and applications of deterministic and stochastic optimization problems, and to give various application examples in business.

Learning Outcomes of the Course Unit

1   Have a knowledge understanding of the basic elements of Mathematical Programming, including the decision variables, the objective function, and the constraints.
2   Have a knowledge understanding of the graphical solution to Linear Programming problems.
3   Formulate linear and non-linear optimizations problems into programs.
4   Verify that a solution to an optimization problem is optimal.
5   Perform sensitivity analysis and understand the information it conveys.
6   Be able to use Spreadsheet Software and a solver to solve and analyze various optimization problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Orientation about Linear Programming (LP) and Linear Algebra Review
2 LP: The Simplex method
3 LP: Duality and Sensitivity Analysis
4 Combinatorial Optimization: Dynamic Programming Approaches & Meta-heuristics
5 Quadratic Programming
6 Nonlinear optimization
7 Network optimization
8 Integer Programming
9 Artificial Neural Networks in Optimization and Applications
10 Markov chains, Markov Decision Processes
11 Stochastic Optimization
12 Data Envelopment Analysis

Recomended or Required Reading

1. Text Books:
Handbook of Applied Optimization, Pardalos, P.M and Resende, M.C.C., 2002, Oxford University Press.
2. Software:
Spreadsheet Software with Solver add-in.

Planned Learning Activities and Teaching Methods

1. Lectures
2. Review Sessions and Class Discussions
3. Computer Applications
4. Case Analysis

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.20 + FIN* 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.20 + RST* 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

1. Grade for Student Participation will depend on (i) class attendance, (ii) the quality of the answers student provide to questions posed by the instructor during class, and (iii) the general contribution the student make to the creation of a positive learning environment.
2. A good attendance record will bring the grade up one level, for grades on the boundary between two grade levels.
3. The case analysis requires a systematic and analytic thinking to integrate all business functions and formulate strategies that will enable businesses to succeed in the business environment. It is the responsibility of the student to contribute to class discussions actively. The contribution will be evaluated for such factors as apparent understanding of the topic, originality and clarity of discussion, comprehensiveness of the solution strategy formulated.

Language of Instruction

English

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.
2. Violations of Plagiarism of any kind will result in disciplinary steps being taken.
3. Absence will not be considered an excuse for submitting homework assignments late.
4. Delayed case reports will suffer grade decay equivalent to one letter grade per day late.

Contact Details for the Lecturer(s)

sabri.erdem@deu.edu.tr

Office Hours

TBA

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparation for midterm exam 1 40 40
Preparation for final exam 1 40 40
Preparations before/after weekly lectures 12 3 36
Preparing assignments 10 4,5 50
Final 1 1,5 2
Midterm 1 1,5 2
TOTAL WORKLOAD (hours) 206

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.1355
LO.2555
LO.355
LO.455
LO.5555
LO.655