|
|
| ENDL5120 | Meta-Heuristic Optimization | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of INDUSTRIAL ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Kemal ÇAKAR | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | A large part of the research area of industrial engineering includes NP-hard problems. These problems usually can not be solved by exact optimization techniques. In recent years, heuristic techniques will be effectively deal with these problems. In this course, heuristic techniques and its application areas will be introduced. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Student learns the basic concepts of heuristic methods | 2,4,5,7 | 1,3 | | PO - 2 : | Student gains the ability of identificating problems and finding solutions by using a mathematical model | 2,4,5,7 | 1,3 | | PO - 3 : | Student gains the ability of improving classical and heuristic methods for the solution of NP-Hard problems | 2,4,5,7 | 1,3 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Introduction to Optimization problems, NP-Complete problems, , Meta-heuristic Methods (Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony) |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Optimization, Optimization Methods | | | Week 2 | Classification of Optimization Methods; Heuristics and Metaheuristics Methods | | | Week 3 | Introduction to (meta) heuristic methods | | | Week 4 | Simulated Annealing Part 1 | | | Week 5 | Simulated Annealing Part 2 | | | Week 6 | Genetic Algorithms Part 1 | | | Week 7 | Genetic Algorithms Part 2 | | | Week 8 | Midterm Exam | | | Week 9 | Tabu Search Algorithms Part 1 | | | Week 10 | Tabu Search Algorithms Part 2 | | | Week 11 | Ant Colony Optimization Algorithm Part 1 | | | Week 12 | Ant Colony Optimization Algorithm Part 2 | | | Week 13 | Bee Colony Optimization Algorithm | | | Week 14 | Student Presentation 1 | | | Week 15 | Student Presentation 2 | | | Week 16 | Final Exam | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | | 1,5 | 30 | | Homework/Assignment/Term-paper | 12 | 14/05/2019 | 1 | 20 | | End-of-term exam | 16 | | 1,5 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Arasınav için hazırlık | 10 | 1 | 10 | | Arasınav | 1.5 | 1 | 1.5 | | Ödev | 10 | 1 | 10 | | Dönem sonu sınavı için hazırlık | 10 | 1 | 10 | | Dönem sonu sınavı | 1.5 | 1 | 1.5 | | Total work load | | | 75 |
|