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 | 4 | 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
Kombinatoryal optimizasyon problemleri ve bunların tamsayılı formülasyonları, Değişik formülasyonların alt ve üst sınır sağlama kapasiteleri bakımından karşılaştırılması, Kesici düzlemler, dal-ve-sınır ve dal-ve-kesi temel çözüm yöntemleri, Problemlerin hesapsal karmaşıklığı ve algoritmik karmaşıklık, Büyük boyutlu problemler için ayrıştırma teknikleri
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 | Simpleks yöntemi ve türevlerini kullanma becerisi kazanır. | P.2.113 | 1 |
| O2 | Sütun oluşturma ve ayrıştırma anlayışı kazanır. | P.2.114 | 1 |
| O3 | Dışbükeylik, minimum, maksimum ve eyer noktaları gibi fonksiyonların matematiksel özelliklerini bilir. | P.2.115 | 1 |
| O4 | Kısıtsız optimizasyon ve çözüm yaklaşımlarını bilir. | 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 Sınavına Hazırlık |
Textbook or Material
| Resources | INTRODUCTION TO OPTIMUM DESIGN, JASBIR S. ARORA, Elsevier |
| INTRODUCTION TO OPTIMUM DESIGN, JASBIR S. ARORA, Elsevier |
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 | Simpleks yöntemi ve türevlerini kullanma becerisi kazanır. | 5 |
| O2 | Sütun oluşturma ve ayrıştırma anlayışı kazanır. | 5 |
| O3 | Dışbükeylik, minimum, maksimum ve eyer noktaları gibi fonksiyonların matematiksel özelliklerini bilir. | 5 |
| O4 | Kısıtsız optimizasyon ve çözüm yaklaşımlarını bilir. | 5 |