COURSE UNIT TITLE

: APPLIED OPTIMIZATION

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DBA 6403 APPLIED OPTIMIZATION ELECTIVE 3 0 0 6

Offered By

Business Administration (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR GÜZIN ÖZDAĞOĞLU

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 Basic Concepts
2 Orientation about Linear Programming (LP)
3 Spreadsheet Modeling Excel Solver Sensitivity Report Other Solution Platforms
4 LP Cases in Business-1
5 LP Cases in Business-2
6 Transportation Problem
7 Network Models-1
8 Network Models-2
9 Integer Programming-1
10 Integer Programming-2
11 Nonlinear Programming-1
12 Nonlinear Programming-2
13 Selected Topics in Applied Optimization
14 Selected Topics in Applied Optimization

Recomended or Required Reading

Text Book(s)/ References / Materials
1. Text Books:

Management Science Modeling, Albright and Winston, 4th edition or later, South-Western Cengage Learning.
(Alternativly: Practical Management Science, Albright and Winston, 4th edition or later, South-Western Cengage Learning.)

Handbook of Applied Optimization, Pardalos, P.M and Resende, M.C.C., 2002, Oxford University Press.

2. Software:
Spreadsheet Software with Solver add-in. Python orR libraries.

Planned Learning Activities and Teaching Methods

1. Lectures
Class lecture is highly interactive and format is direct. The instructor prompts students for response to questions
posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of basic optimization
concepts and techniques where comprehension is substantially enhanced by additional elaboration and
illustration. The emphasis is on the modeling of business problems and their solutions using computer software rather than rigorous mathematics.

2. Computer Applications
Lectures will be carried out at the computer laboratory using spreadsheet software with solver add-in that will be introduced to build optimization models. Instruction on the use of this software as it relates to optimization problems will be provided in class applications with the cases in the book.

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 * 0.30 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40


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

Further Notes About Assessment Methods

None

Assessment Criteria

Student Participation will be assessed depending 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.

A good attendance record will bring the grade up one level, for grades on the boundary between two grade levels.

The term work 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.

Following criteria are considered to assess the assignments:
1. The student is able to clearly identify the problem (5 pts).
2. The student can organize the quantitative data given in the case study (5 pts).
3. The student is able to define appropriate set of variables (10 pts).
4. The student is able to formulate the objective function (10 pts).
5. The student is able to formulate the constraints (10 pts).
6. The student is able to write a complete mathematical model with respect to standard form (10 pts).
7. The student is able to select the technique which can solve the model (5 pts).
8. The student is able to formulate the model on a spreadsheet model (20 pts).
9. The student is able to solve the selected model using appropriate tools and technologies (e.g., optimization tools) (5 pts).
10. The student is able to interpret the outputs of the solution (20 pts).


Language of Instruction

English

Course Policies and Rules

Academic integrity is to demonstrate responsbile and honest behaviors and follow ethical principles in academia. All students should respect the intellectual property rights of others. Specifically every student should avoid plagiarism. All types of plagiarism are serious and violate academic integrity policy.

To understand and prevent plagiarism, please see the following link: https://www.plagiarism.org/understanding-plagiarism.

Contact Details for the Lecturer(s)

guzin.kavrukkoca@deu.edu.tr

Office Hours

TBA

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparations before/after weekly lectures 13 2 26
Preparing assignments 7 5 35
Preparing presentations 1 3 3
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 142

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5
LO.15
LO.25
LO.353
LO.45
LO.55
LO.653