Industrial Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15271734 | Quantitative Techniques in Industrial Engineering | 4 | Autumn | 7 | 3+0+0 | 0 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Prof. Murat DARÇIN |
| Instructor(s) | Asst. Prof. Esra BOZ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Esra BOZ | A-306 | [email protected] | 7677 |
Course Content
Derste işlenecek temel konular şunlardır: tahmin yöntemlerine giriş, basit ve hareketli tahmin yöntemleri, Box-Jenkins tahmin süreçleri, durağan ve dinamik ekonomik tahmin yöntemleri ve endüstriyel tahmin yöntemleri.
Objectives of the Course
In production planning and logistics management, it is important to eliminate the estimation of the future to some extent and to make plans and programs more realistic. The aim of this course is to provide students with knowledge about estimation methods.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | Sufficient knowledge of mathematics, science and Industrial Engineering discipline-specific subjects; Ability to use theoretical and applied knowledge in these fields to solve complex Industrial Engineering problems | 5 |
| P2 | Ability to identify, formulate and solve complex Industrial Engineering problems; Ability to select and apply appropriate analysis and modeling methods for this purpose | 5 |
| P3 | In the field of Industrial Engineering, the ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; Ability to apply modern design methods for this purpose | 5 |
| P11 | Information about the effects of applications in the field of Industrial Engineering on health, environment and security at universal and social dimensions and the problems of the age reflected in the field of engineering; Awareness of the legal consequences of engineering solutions | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Endüstri Mühendisliğinde kantitatif tekniklerle ilgili genel terimlere hâkim olur. | P.1.34 | 1 |
| O2 | Doğrusal programlama, doğrusal olmayan programlama, oyun teorisi, dinamik programlama konularına hâkim olur ve problemlerini çözebilir. | P.1.35 | 1 |
| O3 | Karar problemlerinin çözüm yöntemleri hakkında bilgi sahibi olur. | P.1.36 | 1 |
| O4 | Doğrusal programlama, doğrusal olmayan programlama, oyun teorisi, dinamik programlama konularına hâkim olur ve problemlerini çözebilir. | P.2.52 | 1 |
| O5 | Karşılaşılan gerçek hayat problemleri için matematiksel model kurabilir. | P.2.53 | 1 |
| O6 | Karar problemlerinin çözüm yöntemleri hakkında bilgi sahibi olur. | P.2.54 | 1 |
| O7 | Doğrusal programlama, doğrusal olmayan programlama, oyun teorisi, dinamik programlama konularına hâkim olur ve problemlerini çözebilir. | P.3.30 | 1 |
| O8 | Karşılaşılan gerçek hayat problemleri için matematiksel model kurabilir. | P.3.31 | 1 |
| O9 | Karşılaşılan gerçek hayat problemleri için matematiksel model kurabilir. | P.11.11 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction |
| 2 | Decision Theory |
| 3 | Linear programming applications |
| 4 | Linear programming applications |
| 5 | Linear programming applications |
| 6 | Linear programming applications |
| 7 | Network model formulations |
| 8 | Midterm |
| 9 | Convex and concave functions |
| 10 | Nonlinear programming |
| 11 | Nonlinear programming |
| 12 | Stochastic programming applications |
| 13 | Presentations |
| 14 | Presentations |
| 15 | Final Sınavına Hazırlık |
Textbook or Material
| Resources | Pecar, B., Davis, G., Lillystone, S., Business Forecasting for Management, Mcgraw Hill, 1994 |
| Pecar, B., Davis, G., Lillystone, S., Business Forecasting for Management, Mcgraw Hill, 1994 | |
| Pecar, B., Davis, G., Lillystone, S., Business Forecasting for Management, Mcgraw Hill, 1994 | |
| Pecar, B., Davis, G., Lillystone, S., Business Forecasting for Management, Mcgraw Hill, 1994 |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 3 | 42 |
| Midterms | 1 | 30 | 30 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 36 | 36 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P2 | P3 | P11 |
|---|---|---|---|---|---|
| O1 | Endüstri Mühendisliğinde kantitatif tekniklerle ilgili genel terimlere hâkim olur. | 5 | - | - | - |
| O2 | Doğrusal programlama, doğrusal olmayan programlama, oyun teorisi, dinamik programlama konularına hâkim olur ve problemlerini çözebilir. | 5 | - | - | - |
| O3 | Karar problemlerinin çözüm yöntemleri hakkında bilgi sahibi olur. | 5 | - | - | - |
| O4 | Doğrusal programlama, doğrusal olmayan programlama, oyun teorisi, dinamik programlama konularına hâkim olur ve problemlerini çözebilir. | - | 5 | - | - |
| O5 | Karşılaşılan gerçek hayat problemleri için matematiksel model kurabilir. | - | 5 | - | - |
| O6 | Karar problemlerinin çözüm yöntemleri hakkında bilgi sahibi olur. | - | 5 | - | - |
| O7 | Doğrusal programlama, doğrusal olmayan programlama, oyun teorisi, dinamik programlama konularına hâkim olur ve problemlerini çözebilir. | - | - | 5 | - |
| O8 | Karşılaşılan gerçek hayat problemleri için matematiksel model kurabilir. | - | - | 5 | - |
| O9 | Karşılaşılan gerçek hayat problemleri için matematiksel model kurabilir. | - | - | - | 5 |