Course Name Code Semester T+U Hours Credit ECTS
Sector Oriented Erp Applications ENM 453 7 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Optional
Course Coordinator Dr.Öğr.Üyesi NEVRA AKBİLEK
Course Lecturers Dr.Öğr.Üyesi NEVRA AKBİLEK,
Course Assistants
Course Category
Course Objective
Course Content
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Be informed about introduction to optimization Lecture, Question-Answer, Discussion, Oral Exam, Homework,
2 Be informed about simulated annealing Lecture, Question-Answer, Discussion, Problem Solving, Homework,
3 Be informed about genetic algorithm Lecture, Question-Answer, Problem Solving, Project Based Learning, Homework,
4 Be informed about tabu search Lecture, Question-Answer, Discussion, Problem Solving, Project Based Learning, Homework,
5 Be informed about ant colony Lecture, Question-Answer, Discussion, Problem Solving, Project Based Learning, Homework,
6 Be informed about hybrid methods Lecture, Question-Answer, Discussion, Problem Solving, Project Based Learning, Homework,
7 Be informed about evolutionary Algorithms Lecture, Question-Answer, Discussion, Problem Solving, Project Based Learning, Homework,
8 Be informed about particle swarm optimization Lecture, Question-Answer, Discussion, Problem Solving, Project Based Learning, Homework,
9 Be informed about constraint handling methods in evolutioanry algortihm Lecture, Question-Answer, Discussion, Problem Solving, Project Based Learning, Homework,
10 Be informed about multi-objective optimization Lecture, Question-Answer, Discussion, Problem Solving, Project Based Learning, Homework,
11 Be informed about current optimization applications in literature Lecture, Question-Answer, Discussion, Case Study, Self Study, Homework,
12 Project implementation is improved Lecture, Question-Answer, Discussion, Self Study, Problem Solving, Project Based Learning, Project / Design,
Week Course Topics Preliminary Preparation
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
Resources
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
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.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 25
1. Proje / Tasarım 60
1. Performans Görevi (Uygulama) 10
1. Ödev 5
Total 100
1. Yıl İçinin Başarıya 70
1. Final 30
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 14 3 42
Mid-terms 1 5 5
Quiz 1 1 1
Project / Design 1 16 16
Final examination 1 6 6
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Assignment 1 5 5
Performance Task (Application) 1 3 3
Total Workload 126
Total Workload / 25 (Hours) 5.04
dersAKTSKredisi 5