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 | 2025 | 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
The main topics to be covered in the course are: introduction to forecasting methods, simple and moving average forecasting methods, Box-Jenkins forecasting processes, static and dynamic economic forecasting methods, and industrial forecasting methods.
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 | Knowledge of mathematics, natural sciences, fundamental engineering, computational sciences, and industrial engineering-specific subjects; the ability to apply this knowledge to solve complex industrial engineering problems. | 5 |
| P2 | The ability to define, formulate, and analyze complex industrial engineering problems using fundamental science, mathematics, and engineering knowledge, while keeping in mind the relevant UN Sustainable Development Goals. | 5 |
| P3 | The ability to design creative solutions to complex industrial engineering problems; the ability to design complex systems, processes, devices, or products to meet current and future requirements, while considering realistic constraints and conditions. | 5 |
| P6 | Information about the impacts of engineering applications on society, health and safety, the economy, sustainability, and the environment within the framework of the UN Sustainable Development Goals; 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 | Demonstrate knowledge of general terms related to quantitative techniques in industrial engineering. | P.1.34 | 1 |
| O2 | Master linear programming, nonlinear programming, game theory, and dynamic programming and solve related problems. | P.1.35 | 1 |
| O3 | Demonstrate knowledge of decision problem-solving methods. | P.1.36 | 1 |
| O4 | Master linear programming, nonlinear programming, game theory, and dynamic programming and solve related problems. | P.2.52 | 1 |
| O5 | Formulate mathematical models for real-life problems. | P.2.53 | 1 |
| O6 | Demonstrate knowledge of decision problem-solving methods. | P.2.54 | 1 |
| O7 | Master linear programming, nonlinear programming, game theory, and dynamic programming and solve related problems. | P.3.30 | 1 |
| O8 | Formulate mathematical models for real-life problems. | P.3.31 | 1 |
| O9 | Formulate mathematical models for real-life problems. | P.6.11 | 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 | Akdi, Y., Zaman Serileri Analizi, Gazi Kitabevi, Ankara, 2010 |
| Erdemir, C., Kadılar, C., 2003, Benzetim Tekniklerine Giriş, Hacettepe | |
| Makridakis S., Wheelwright S, C., McGee V, E., ""Forecasting: Methods and Applications"", John Wiley, Third edition, 1998 | |
| 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 | P6 |
|---|---|---|---|---|---|
| O1 | Demonstrate knowledge of general terms related to quantitative techniques in industrial engineering. | 5 | - | - | - |
| O2 | Master linear programming, nonlinear programming, game theory, and dynamic programming and solve related problems. | 5 | - | - | - |
| O3 | Demonstrate knowledge of decision problem-solving methods. | 5 | - | - | - |
| O4 | Master linear programming, nonlinear programming, game theory, and dynamic programming and solve related problems. | - | 5 | - | - |
| O5 | Formulate mathematical models for real-life problems. | - | 5 | - | - |
| O6 | Demonstrate knowledge of decision problem-solving methods. | - | 5 | - | - |
| O7 | Master linear programming, nonlinear programming, game theory, and dynamic programming and solve related problems. | - | - | 5 | - |
| O8 | Formulate mathematical models for real-life problems. | - | - | 5 | - |
| O9 | Formulate mathematical models for real-life problems. | - | - | - | 5 |