1663 Industrial Engineering Phd

GENERAL INFORMATION
The Industrial Engineering PhD Program has been designed and operated by the Department of Industrial Engineering. The program accepted its first students in the academic year of 2013-2014. The language of the program is Turkish. In order for candidates to be accepted into the program they are required to meet necessary criteria set out by the Natural Sciences Institute by PAU and pass the required exams. The main principle of the program is to provide an up-to-date, practical and research oriented engineering education and carry out high quality scientific research. Most-up-to-date information regarding the programs can be gathered through http://endustri.pau.edu.tr or PAU Education Information System.

Objective
In brief, the program’s teaching goals can be summarized as follows: We aim to give a sufficient and quality education and training to our students, to be able do the following when they graduate: To manage activities related to productivity and quality improvement, To design and manage industrial and/or service systems, To create solid decisions based on analytical approaches and scientific analyses, To create and manage information and production integration projects, To plan operational activities in industrial and/or service systems, To learn and understand the standards related to quality, work safety and environment and integrate them into decision making process, and To have good communication skills to share and gather and generate scientific knowledge on a global level.


Admission Requirements
First cycle degree, acceptable score on centralized graduate entrance exam, placement through local oral/written exam and certificate of English proficiency

Graduation Requirements
A student must complete the required course load (21 PAU credits) with a Cumulative Grade Point Average (CGPA) of at least 3.00/4.00; present a research seminar, prepare and defend a Doctora thesis successfully.

Career Opportunties
Graduates who have successfully completed their Ph.D. program can either work in an academic position in higher education institutions in the country or abroad in the same or similar fields, or they can continue their independent research. They can also take positions in private sector, public institutions and research centers in senior positions.

Qualification Awarded
Industrial Engineering Phd.

Level of Qualification
Third Cycle (Doctorate Degree)

Recognition of Prior Learning
A successful student who has completed at least one semester in another institution / department of the university or another post-graduate program of another higher education institution may be admitted to the post-graduate programs by horizontal transfer. The conditions for acceptance by horizontal transfer are determined by the Senate.

Qualification Requirements and Regulations
A student must complete the required course load (21 PAU credits) with a Cumulative Grade Point Average (CGPA) of at least 3.00/4.00; present a research seminar, prepare and defend a Doctora thesis successfully.

Access to Further Studies
A student graduated with a good PhD Degree may carry on an academic carrier as a lecturer or post doctorate researcher.

Mode of Study
Full Time

Examination Regulations, Assessment and Grading
Measurement and evaluation methods that is applied for each course, is detailed in "Course Structure&ECTS Credits".

Contact (Programme Director or Equivalent)
PositionName SurnamePhoneFaxE-Mail
 Prof. Dr. AŞKINER GÜNGÖR+90 258 296 3141 askiner@pau.edu.tr


PROGRAM LEARNING OUTCOMES
1Students gain sufficient basic skills about the topics of Mathematics, Science and Industrial Engineering; the ability to use theoretical and practical knowledge in these areas for engineering solutions.
2Students identify, define, formulate, and solve complex industrial engineering problems; to choose the appropriate analytical methods and model techniques and gain application skills for this purpose.
3Students gain the ability to design a complete or a component of a complex system under realistic constraints related to economy, environment, sustainability, ethics, health, safety, social and politics.
4Students generate, select and/or use the modern techniques in industrial engineering applications; and learn to use information technologies efficiently.
5Students learn to design experiments, do experiments, collect data, analyze and interpret findings when dealing with industrial engineering problems.
6Students gain skills to work efficiently and manage a multi-disciplined team while focusing on his/her own responsibility.
7Students monitor the developments in science and technology and constantly renew themselves by giving importance to life-long-learning.
8Students gain skills to manage entrepreneurship, innovation and sustainability related risk and change management projects.
9Students become aware of the effects of engineering solutions on health, environment and safety and gain up-to-date knowledge on related issues.
10Students gain the ability to analyze the theoretical research and determine the novel research problems.
TEACHING & LEARNING METHODS
NameComments
LecturingLecturing is one of the methods that come first, where the teacher is in the center. It is a method where the teacher actively describes topics and the students are passive listeners. With this method, lesson proceeds in the form of report, description and explanation.
DebateDepending on the situation, debate is a tool that allows all students, or a specific portion of the class to participate in the lesson. In this method, members of the group discuss a topic by addressing the various points of view and discuss alternative opinions about problem-solving.
DemonstrationIn this method the teacher demonstrates, an experiment, test, in front of the class. And then assists students to do so too. Students learn not only by just by looking and watching, but also by taking part and participating. This method is usually applied when teaching skills.
Case Study Case studies require students to actively participate while using an analytical perspective to think about real and problematic events. The problematic event may be real or very close to real life. Student(s) working on the documents that include the necessary data and descriptions of the
Problem SolvingThe name given to any doubt or ambiguity that arises is, a problem. Problems which usually have a role in human life, that have preventing or annoying aspects are solved by considering the stages of scientific methods. (a) Problems are determined. (b) The problem is identified. 
Cooperative LearningCooperative Learning is; a kind of learning that is based on the students working together for a common purpose. Children with different skills come together in heterogeneous groups to learn by helping each other. Students gain experiences such as becoming aware of the unity
Questions –AnswersThe different types of Questions used (associative, differential, assessment, requesting information, motivating, and brainstorming) although students get in to more active positions during the process; the method is teacher-centered. If possible Questions, that serve a purpose and
Concept MapsExpress a relation network, based on figures, graphics and words propositions and principles. It enables visual learning. These steps are followed: 1) Concepts about the subjects to be taught are listed. 2) The name of the subject that will be taught is written at the top.3) Relations between
Meaning Analysis ChartsThe Meaning Analysis Charts is a method that allows students to participate actively in class. Students learn by working on a two-dimensional table. For example, if in one dimension there are animals and in the other dimension food is shown, students have the opportunity to learn by
Scenario-based teaching Although it is similar to the Case Study method, there is a fictional approach in the scenario. The subject can be presented by inserting it in to a fiction and can also lead the student’s to producing their own scenarios.
Simulation Expresses situations where in real life learning is dangerous, difficult to reach and expensive and where students work on models which are very similar to the real thing. For example, before airplane pilots and astronauts embark their aircrafts and spacecrafts, they perform applications
Role Playing Role-playing is a learning way which helps students to express their own feelings and thoughts by playing the role of other personalities. It is necessary that students use creative thinking to succeed. Students put themselves in someone’s place by purifying themselves from their actual
Drama Drama is a method in which students learn a skill or situation by reenacting in front of the class. As well as gaining knowledge by experience, it has important effects on the development of verbal expression and socialization. It enables us to bring up individuals who are creative, productive,
ProjectProject-based learning is a learning way which leads students to deal with interesting problems and to create extraordinary products at the end of this. It allows students to use their creativity and it requires them to look at events perceptively.
Technical TourIs a method that takes learning to the out of the classroom. It is a method that provides students to make direct observations and to gain information by taking them to places, such as factories, museums, libraries, various government agencies, mountains, forests, lakes, parks and gardens.
Observation Although we generally get information related to the nature through observations, the Observation method can also be used for other situations and under other conditions. We try to reach certain generalizations by thinking about our findings which we gather from our observations.
Testing Means reaching results by using various information with certain mechanisms, which are set up to imitate natural events in artificial environments and to have students take a certain topics and applying them to reach certain aims/objectives. Nature researchers, scientists, and educators
InterviewThis is when the teacher brings in (writers, artists, designers, writers, illustrators, etc.) because he/she is not equipped with the adequate facilities to do with certain issues or situations. It is a technique that triggers the senses of students. In some cases, students interview certain people
Programmed InstructionAt its basis lays the Individualization of instruction. Programmed instruction is an individual teaching technique, guided by the reinforcement principles of Skinner. Its Basic principles are: the principle of small steps, the principle of effective participation, the achievement principle, the
Brainstorming Brainstorming is a group work process that has been regulated to reach solutions for a problem without limitations or evaluation. The purpose of brainstorming is to make it easier for students to express themselves and to generate ideas. This technique is used as a high-level discussion

Learning Outcomes - NQF-HETR Relation
NQF-HETR CategoryNQF-HETR Sub-CategoryNQF-HETRLearning Outcomes
INFORMATION  01
INFORMATION  02
SKILLS  01
SKILLS  02
SKILLS  03
SKILLS  04
COMPETENCIESCommunication and Social Competence 01
COMPETENCIESCommunication and Social Competence 02
COMPETENCIESCommunication and Social Competence 03
COMPETENCIESCompetence to Work Independently and Take Responsibility 01
COMPETENCIESCompetence to Work Independently and Take Responsibility 02
COMPETENCIESCompetence to Work Independently and Take Responsibility 03
COMPETENCIESField Specific Competencies 01
COMPETENCIESField Specific Competencies 02
COMPETENCIESField Specific Competencies 03
COMPETENCIESLearning Competence 01
    

Learning Outcomes - Fields of Education Relation (Academic)
FOE CategoryFOE Sub-CategoryFOELearning Outcomes
INFORMATION  01
INFORMATION  02
SKILLS  01
SKILLS  02
SKILLS  03
SKILLS  04
SKILLS  05
COMPETENCIESCommunication and Social Competence 01
COMPETENCIESCommunication and Social Competence 02
COMPETENCIESCompetence to Work Independently and Take Responsibility 01
COMPETENCIESCompetence to Work Independently and Take Responsibility 02
COMPETENCIESCompetence to Work Independently and Take Responsibility 03
COMPETENCIESField Specific Competencies 01
COMPETENCIESField Specific Competencies 02
COMPETENCIESLearning Competence 01
COMPETENCIESLearning Competence 02
COMPETENCIESLearning Competence 03
COMPETENCIESLearning Competence 04
    

Learning Outcomes - Fields of Education Relation (Vocational)
No Records to Display

COURS STRUCTURE & ECTS CREDITS
Year :
1st Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 611 STOCHASTIC PROCESSES 3+0 7,5 Compulsory
- Elective 3+0 7,5 Elective
- Elective 3+0 7,5 Elective
- Elective 3+0 7,5 Elective
  Total 30  
1st Semester Elective Groups : Elective
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 501 OPTIMIZATION TECHNICS I 3+07,5Elective
ENM 503 STATISTICAL DESIGN OF EXPERIMENT 3+07,5Elective
ENM 504 DECISION ANALYSIS AND MULTI-CRITERIA DECISION MAKING 3+07,5Elective
ENM 505 SUPPLY CHAIN AND LOGISTICS MANAGEMENT 3+07,5Elective
ENM 506 OPTIMIZATION TECHNICS II 3+07,5Elective
ENM 507 HEURISTIC METHODS AND APPLICATIONS 3+07,5Elective
ENM 509 NETWORK OPTIMIZATION 3+07,5Elective
ENM 510 QUALITY ENGINEERING 3+07,5Elective
ENM 511 ADVANCED PRODUCTION PLANNING AND CONTROLLING 3+07,5Elective
ENM 512 PROJECT MANAGEMENT IN ENGINEERING 3+07,5Elective
ENM 513 COMPETITIVE PRODUCTION MANAGEMENT APPROACHES 3+07,5Elective
ENM 515 NETWORK OPTIMIZATION 3+07,5Elective
ENM 517 ADVENCED SIMULATION 3+07,5Elective
ENM 523 ADVANCED INVENTORY MANAGEMENT 3+07,5Elective
ENM 524 ADVENCED INVENTORY MANAGEMENT 3+07,5Elective
ENM 526 TIME MANAGEMENT ANALYSIS AND FORECASTING TECHNIQUES 3+07,5Elective
ENM 528 FUZZY SET THEORY 3+07,5Elective
ENM 530 LOCATION AND LAYOUT OPTIMIZATION 3+07,5Elective
ENM 531 NONLINEAR PROGRAMMING 3+07,5Elective
ENM 532 QUEUEING SYSTEMS 3+07,5Elective
ENM 550 INDUSTRIAL OPTIMIZATION 3+07,5Elective
ENM 555 MATLAB WITH APPLICATIONS 3+07,5Elective
ENM 560 SERVICE SYSTEMS OPTIMIZASION 3+07,5Elective
ENM 561 RELIABILITY ENGINEERING 3+07,5Elective
ENM 562 FUZZY APPLICATIONS IN DECISION MAKING 3+07,5Elective
ENM 563 ADVANCED LOGISTICS OPTIMIZATION 3+07,5Elective
ENM 564 DATA MINING 3+07,5Elective

2nd Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 698 DOCTORATE SEMINAR - I 0+2 7,5 Compulsory
ENM 508 SEQUENCING AND SCHEDULING IN MANUFACTURING 3+0 7,5 Compulsory
- Elective 3+0 7,5 Elective
- Elective 3+0 7,5 Elective
  Total 30  
2nd Semester Elective Groups : Elective
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 501 OPTIMIZATION TECHNICS I 3+07,5Elective
ENM 503 STATISTICAL DESIGN OF EXPERIMENT 3+07,5Elective
ENM 504 DECISION ANALYSIS AND MULTI-CRITERIA DECISION MAKING 3+07,5Elective
ENM 505 SUPPLY CHAIN AND LOGISTICS MANAGEMENT 3+07,5Elective
ENM 506 OPTIMIZATION TECHNICS II 3+07,5Elective
ENM 507 HEURISTIC METHODS AND APPLICATIONS 3+07,5Elective
ENM 509 NETWORK OPTIMIZATION 3+07,5Elective
ENM 510 QUALITY ENGINEERING 3+07,5Elective
ENM 511 ADVANCED PRODUCTION PLANNING AND CONTROLLING 3+07,5Elective
ENM 517 ADVENCED SIMULATION 3+07,5Elective
ENM 523 ADVANCED INVENTORY MANAGEMENT 3+07,5Elective
ENM 524 ADVENCED INVENTORY MANAGEMENT 3+07,5Elective
ENM 526 TIME MANAGEMENT ANALYSIS AND FORECASTING TECHNIQUES 3+07,5Elective
ENM 528 FUZZY SET THEORY 3+07,5Elective
ENM 530 LOCATION AND LAYOUT OPTIMIZATION 3+07,5Elective
ENM 532 QUEUEING SYSTEMS 3+07,5Elective
ENM 550 INDUSTRIAL OPTIMIZATION 3+07,5Elective
ENM 555 MATLAB WITH APPLICATIONS 3+07,5Elective
ENM 560 SERVICE SYSTEMS OPTIMIZASION 3+07,5Elective
ENM 561 RELIABILITY ENGINEERING 3+07,5Elective
ENM 562 FUZZY APPLICATIONS IN DECISION MAKING 3+07,5Elective
ENM 563 ADVANCED LOGISTICS OPTIMIZATION 3+07,5Elective
ENM 564 DATA MINING 3+07,5Elective

3rd Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
FBE 897 DEVELOPMENT AND LEARNING 3+0 7,5 Compulsory
FBE 896 PLANNING AND ASSESSMENT IN EDUCATION 3+2 7,5 Compulsory
ENM 699 DOCTORATE SEMINAR - II 0+2 7,5 Compulsory
FBE 610 METHODS OF RESEARCH AND ETHICS 3+0 7,5 Compulsory
  Total 30  

4th Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
ENS 600 PROFICIENCY EXAM PREPARATION 0+0 20 Compulsory
ENS 602 THESIS PROPOSAL PREPARATION 0+0 10 Compulsory
  Total 30  

5th Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 800 DOCTORATE EXPERTISE FIELD COURSES 8+0 10 Compulsory
ENM 600 DOCTORATE THESIS 0+0 20 Compulsory
  Total 30  

6th Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 800 DOCTORATE EXPERTISE FIELD COURSES 8+0 10 Compulsory
ENM 600 DOCTORATE THESIS 0+0 20 Compulsory
  Total 30  

7th Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 800 DOCTORATE EXPERTISE FIELD COURSES 8+0 10 Compulsory
ENM 600 DOCTORATE THESIS 0+0 20 Compulsory
  Total 30  

8th Semester Course Plan
Course CodeCourse TitleL+P HourECTSCourse Type
ENM 800 DOCTORATE EXPERTISE FIELD COURSES 8+0 10 Compulsory
ENM 600 DOCTORATE THESIS 0+0 20 Compulsory
  Total 30  


COURSE & PROGRAM LEARNING OUTCOMES
Year : Compulsory Courses
Course TitleC/EPO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10
DEVELOPMENT AND LEARNINGC          
DOCTORATE EXPERTISE FIELD COURSESC**********
DOCTORATE SEMINAR - IC**********
DOCTORATE SEMINAR - IIC**********
DOCTORATE THESISC          
GRADUATE COUNSELINGC          
METHODS OF RESEARCH AND ETHICSC          
PLANNING AND ASSESSMENT IN EDUCATIONC**********
PROFICIENCY EXAM PREPARATIONC          
SEQUENCING AND SCHEDULING IN MANUFACTURINGC**********
STOCHASTIC PROCESSESC          
THESIS PROPOSAL PREPARATIONC          
Click to add elective courses...
Elective Courses
Course TitleC/EPO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10
ADVANCED INVENTORY MANAGEMENTE**********
ADVANCED LOGISTICS OPTIMIZATIONE**********
ADVANCED PRODUCTION PLANNING AND CONTROLLINGE**********
ADVENCED INVENTORY MANAGEMENTE**********
ADVENCED SIMULATIONE**********
COMPETITIVE PRODUCTION MANAGEMENT APPROACHESE          
DATA MININGE**********
DECISION ANALYSIS AND MULTI-CRITERIA DECISION MAKINGE**********
FUZZY APPLICATIONS IN DECISION MAKINGE**********
FUZZY SET THEORYE**********
HEURISTIC METHODS AND APPLICATIONSE**********
INDUSTRIAL OPTIMIZATIONE**********
LOCATION AND LAYOUT OPTIMIZATIONE**********
MATLAB WITH APPLICATIONSE**********
NETWORK OPTIMIZATIONE**********
NONLINEAR PROGRAMMINGE          
OPTIMIZATION TECHNICS IE**********
OPTIMIZATION TECHNICS IIE**********
PROJECT MANAGEMENT IN ENGINEERINGE          
QUALITY ENGINEERINGE**********
QUEUEING SYSTEMSE**********
RELIABILITY ENGINEERINGE**********
SERVICE SYSTEMS OPTIMIZASION E          
STATISTICAL DESIGN OF EXPERIMENTE**********
SUPPLY CHAIN AND LOGISTICS MANAGEMENTE**********
TIME MANAGEMENT ANALYSIS AND FORECASTING TECHNIQUESE**********
L+P: Lecture and Practice
C: Compulsory
E: Elective
PQ: Program Learning Outcomes
TH [5]: Too High
H [4]: High
M [3]: Medium
L [2]: Low
TL [1]: Too Low
None [0]: None
FOE [0]: Field of Education
NQF-HETR : National Qualifications Framework For Higher Education in Turkey
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