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Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
SECTOR ORIENTED ERP APPLICATIONS ENM 453 7 3 + 0 3 5
Ön Koşul Dersleri
Önerilen Seçmeli Dersler
Dersin Dili Türkçe
Dersin Seviyesi Lisans
Dersin Türü SECMELI
Dersin Koordinatörü Dr.Öğr.Üyesi NEVRA AKBİLEK
Dersi Verenler Dr.Öğr.Üyesi NEVRA AKBİLEK
Dersin Yardımcıları
Dersin Kategorisi
Dersin Amacı
Dersin İçeriği
Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri
1 - Be informed about introduction to optimization 1 - 2 - 3 - B - C -
2 - Be informed about simulated annealing 1 - 2 - 3 - 15 - C -
3 - Be informed about genetic algorithm 1 - 2 - 15 - 16 - C -
4 - Be informed about tabu search 1 - 2 - 3 - 15 - 16 - C -
5 - Be informed about ant colony 1 - 2 - 3 - 15 - 16 - C -
6 - Be informed about hybrid methods 1 - 2 - 3 - 15 - 16 - C -
7 - Be informed about evolutionary Algorithms 1 - 2 - 3 - 15 - 16 - C -
8 - Be informed about particle swarm optimization 1 - 2 - 3 - 15 - 16 - C -
9 - Be informed about constraint handling methods in evolutioanry algortihm 1 - 2 - 3 - 15 - 16 - C -
10 - Be informed about multi-objective optimization 1 - 2 - 3 - 15 - 16 - C -
11 - Be informed about current optimization applications in literature 1 - 2 - 3 - 12 - 14 - C -
12 - Project implementation is improved 1 - 2 - 3 - 14 - 15 - 16 - D -
Öğretim Yöntemleri: 1:Lecture 2:Question-Answer 3:Discussion 15:Problem Solving 16:Project Based Learning 12:Case Study 14:Self Study
Ölçme Yöntemleri: B:Oral Exam C:Homework D:Project / Design

Ders Akışı

Hafta Konular ÖnHazırlık
1 Introduction to adaptive search method Artificial Intelligence-Introduction to Heuristic optimization algorithms
2 Simulated annealing Algorithm Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
3 Genetic Algorithm Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
4 Evolutionary Algorithms Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry , Eric Taillard,
5 Tabu Search Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry , Eric Taillard .
6 Ant Colony Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski Patrick Siarry, Eric Taillard
7 Particle swarm optimization Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski, Patrick Siarry, Eric Taillard
8 Hybrid methods Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
9 Constraint handling methods in evolutioanry algortihm Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski, Patrick Siarry , Eric Taillard
10 Multi-objective optimization Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski, Patrick Siarry, Eric Taillard
11 Current optimization applications in literature
12 Evaluation and analysis of existing practices
13 Project: solving a real problem with a meta-heuristic method
14 Evaluating and discussing the developed projects

Kaynaklar

Ders Notu
Ders Kaynakları

Döküman Paylaşımı


Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5
1 Engineering graduates with sufficient knowledge background on science and engineering subjects of their related area, and who are skillful in implementing theoretical and practical knowledge for modelling and solving engineering problems. X
2 Engineering graduates with skills in identifying, describing, formulating and solving complex engineering problems, and thus,deciding and implementing appropriate methods for analyzing and modelling. X
3 Engineering graduates with skills in designing a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; for this purpose, skills in implementing modern design methods. X
4 Engineering graduates with skills in developing, selecting and implementing modern techniques and tools required for engineering applications as well as with skills in using information technologies effectively. X
5 Engineering graduates with skills in designing and conducting experiments, collecting data, analyzing and interpreting the results in order to evaluate engineering problems. X
6 Engineering graduates who are able to work within a one discipline or multi-discipline team,as well as who are able to work individually X
7 Engineering graduates who are able to effectively communicate orally and officially in Turkish Language as well as who knows at least one foreign language X
8 Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology X
9 Engineering graduates with well-structured responsibilities in profession and ethics X
10 Engineering graduates having knowledge about practices in professional life such as project management, risk management and change management, and who are aware of innovation and sustainable development. X
11 Engineering graduates having knowledge about universal and social effects of engineering applications on health, environment and safety, as well as having awareness for juridical consequences of engineering solutions.

Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 25
ProjeTasarim 1 60
PerformansGoreviUygulama 1 10
Odev 1 5
Toplam 100
Yıliçinin Başarıya Oranı 70
Finalin Başarıya Oranı 30
Toplam 100

AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
Course Duration (Including the exam week: 16x Total course hours) 14 3 42
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 5 5
Quiz 1 1 1
Assignment 1 5 5
Project / Design 1 16 16
Performance Task (Application) 1 3 3
Final examination 1 6 6
Toplam İş Yükü 126
Toplam İş Yükü /25(s) 5.04
Dersin AKTS Kredisi 5.04
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