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 |
|---|---|---|---|---|---|---|---|
| 15240401 | Operations Research - II | 2025 | Spring | 4 | 3+1+0 | 3,5 | 6 |
| Course Type | Compulsory |
| 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
Network models; Least Spanning Tree Algorithm; Shortest Path Algorithms; Maximum Flow Algorithm; Least Cost-Capacity Problem Flow and Algorithm; CPM PERT; Specific Inventory Models; Queueing Theory and Queueing Models
Objectives of the Course
To teach theory and solution procedure of Networks Models, Inventory Models and Queuing Models in Operations Research and to find the politics and activities of management as scientific
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | |
| 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 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Able to formulate integer programming models and explain solution methods. | P.1.142 | 1 |
| O2 | Learns and applies a programming language to solve Operations Research problems. | P.1.143 | 1 |
| O3 | Formulates integer programming models and explains their solution methods. | P.2.37 | 1 |
| O4 | Defines the core elements of a decision problem and solves decision problems in different decision environments. | P.2.38 | 1 |
| O5 | Formulates and solves goal programming decision models. | P.2.39 | 1 |
| O6 | Masters game theory and dynamic programming concepts and solves related problems. | P.2.40 | 1 |
| O7 | Analyzes and solves relevant problems within network models. | P.2.41 | 1 |
| O8 | Learns and applies a programming language to solve Operations Research problems. | P.2.42 | 1 |
| O9 | Formulates integer programming models and explains their solution methods. | P.3.18 | 1 |
| O10 | Defines the core elements of a decision problem and solves decision problems in different decision environments. | P.3.19 | 1 |
| O11 | Formulates goal programming decision models. | P.3.20 | 1 |
| O12 | Masters game theory and dynamic programming concepts and gains the ability to solve related problems. | P.3.21 | 1 |
| O13 | Comprehends and solves relevant problems within network models. | P.3.22 | 1 |
| O14 | Learns and applies a programming language to solve Operations Research problems. | P.3.23 | 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 | Integer programming |
| 2 | Integer programming |
| 3 | Integer programming |
| 4 | Network model |
| 5 | Network model |
| 6 | Network model |
| 7 | CPM/PERT |
| 8 | Ara Sınav |
| 9 | CPM/PERT |
| 10 | CPM/PERT |
| 11 | CPM/PERT |
| 12 | CPM/PERT |
| 13 | Inventory models |
| 14 | Inventory models |
| 15 | Queuing Models |
Textbook or Material
| Resources | Frederick S. Hillier ve Gerald J. Lieberman, Introduction to Operations Research, 8th Ed., McGraw-Hill, New York, 2005. |
| Hamdy A. Taha, Yöneylem Araştırması, 6. basım, Ş. Alp Baray ve Şakir Esnaf tarafından 6. basımdan Çeviri, Literatür Yayıncılık, İstanbul, 2003. | |
| Operations Research: Applications and Algorithms, WINSTON W.L, (2004) |
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 | 1 | 30 (%) |
| Listening | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 40 (%) |
| 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 | 4 | 56 |
| Midterms | 1 | 24 | 24 |
| Quiz | 1 | 14 | 14 |
| Homework | 0 | 0 | 0 |
| Practice | 14 | 1 | 14 |
| 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 | 30 | 30 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 180 | ||
| Total Work Load / 30 | 6 | ||
| Course ECTS Credits: | 6 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P2 | P3 |
|---|---|---|---|---|
| O1 | Able to formulate integer programming models and explain solution methods. | 5 | - | - |
| O2 | Learns and applies a programming language to solve Operations Research problems. | 5 | - | - |
| O3 | Formulates integer programming models and explains their solution methods. | - | 5 | - |
| O4 | Defines the core elements of a decision problem and solves decision problems in different decision environments. | - | 5 | - |
| O5 | Formulates and solves goal programming decision models. | - | 5 | - |
| O6 | Masters game theory and dynamic programming concepts and solves related problems. | - | 5 | - |
| O7 | Analyzes and solves relevant problems within network models. | - | 5 | - |
| O8 | Learns and applies a programming language to solve Operations Research problems. | - | 5 | - |
| O9 | Formulates integer programming models and explains their solution methods. | - | - | 5 |
| O10 | Defines the core elements of a decision problem and solves decision problems in different decision environments. | - | - | 5 |
| O11 | Formulates goal programming decision models. | - | - | 5 |
| O12 | Masters game theory and dynamic programming concepts and gains the ability to solve related problems. | - | - | 5 |
| O13 | Comprehends and solves relevant problems within network models. | - | - | 5 |
| O14 | Learns and applies a programming language to solve Operations Research problems. | - | - | 5 |