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 |
|---|---|---|---|---|---|---|---|
| 15281847 | Advanced Optimization | 2025 | Spring | 8 | 3+0+0 | 3 | 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) | - |
| Instructor Assistant(s) | - |
Course Content
Combinatorial optimization problems and their integer formulations, comparison of different formulations in terms of their capacity to satisfy lower and upper bounds, cutting planes, branch-and-bound and branch-and-cut fundamental solution methods, computational complexity and algorithmic complexity of problems, decomposition techniques for large-scale problems.
Objectives of the Course
The aim of this course is to recognize the methods of discrete and combinatorial optimization and to examine the techniques and applications of integer programming.
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 |
|---|---|---|
| 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 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Apply the simplex method and its variants. | P.2.113 | 1 |
| O2 | Understand column generation and decomposition concepts. | P.2.114 | 1 |
| O3 | Understand the mathematical properties of functions such as convexity, minima, maxima, and saddle points. | P.2.115 | 1 |
| O4 | Understand unconstrained optimization and solution approaches. | P.2.116 | 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 to Optimization |
| 2 | Teaching of software necessary for discrete and combinatorial optimization (MATLAB relational and logical operations and basic-mathematical operations) |
| 3 | Teaching of software necessary for discrete and combinatorial optimization (MATLAB loops and functions |
| 4 | Teaching of software necessary for discrete and combinatorial optimization (MATLAB array and matrix operations) |
| 5 | Teaching of software necessary for discrete and combinatorial optimization (MATLAB drawing graph, graph types and graphical optimization) |
| 6 | Teaching of software necessary for discrete and combinatorial optimization (MATLAB ans EXCEL optimization applicatios) |
| 7 | MATLAB Applications |
| 8 | Midterm |
| 9 | Unconstrained Optimization |
| 10 | Constrained Optimization |
| 11 | Solution of integer programming problems: Cutting plane method |
| 12 | Solution of integer programming problems: Cutting plane method |
| 13 | Solution of integer programming problems: Branch-bound method |
| 14 | Discrete heuristic optimization applications |
| 15 | Final Exam Preparation |
Textbook or Material
| Resources | INTRODUCTION TO OPTIMUM DESIGN, JASBIR S. ARORA, Elsevier |
| Optimizasyon ve Matlab Uygulamaları, Aysun Tezel Özturan, Nobel Akademik Yayıncılık |
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 | P2 |
|---|---|---|
| O1 | Apply the simplex method and its variants. | 5 |
| O2 | Understand column generation and decomposition concepts. | 5 |
| O3 | Understand the mathematical properties of functions such as convexity, minima, maxima, and saddle points. | 5 |
| O4 | Understand unconstrained optimization and solution approaches. | 5 |