SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
60549MEEOS-IEN0285	Heuristic Algorithms	0	Spring	2	2	3	5

Course Type :
Cycle:	Bachelor      TQF-HE:6. Master`s Degree      QF-EHEA:First Cycle      EQF-LLL:6. Master`s Degree
Language of Instruction:	English
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Instructor CEM KAZAN
Dersin Öğretim Eleman(lar)ı:	Profesör Dr. NEVZAT EVRİM ÖNAL
Dersin Kategorisi:

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

The difficulty level of many real-world problems in business are considered to be NPComplete. Utilizing conventional optimization techniques in this type of problems is either computationally expensive or do not yield to a result. However, utilizing Heuristic Search algorithms, a near-optimum solution can be found in a short amount of time. This course firstly provides a detailed introduction to Heuristic Search algorithms. Simulated Annealing, Tabu Search, Variable Neighbourhood Search, Genetic Algorithm and Swarm Intelligence will be in the focus. Aims of the course are: - To introduce variety of heuristic methods with field of application - Modeling heuristic algorithms in optimization

Course Content:

Simulated Annealing, Tabu Search, Variable Neighbourhood Search, Genetic Algorithm and Swarm Intelligence

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:

Knowledge (Described as Theoritical and/or Factual Knowledge.)

1) Learn basic properties and structure of heuristic optimization methods

2) Compare metaheuristic optimization methods with classical optimization methods

Skills (Describe as Cognitive and/or Practical Skills.)

1) Learn the mathematical structure and application of heuristic optimization algorithms

2) Learn the structure and application of single-solution based optimization algorithms

3) Learn the mathematical structure and application of evolutionary algorithm

4) Apply algorithms to the optimization problems related to industrial engineering

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Syllabus
2)	Common Concepts
3)	Simulated Annealing
4)	Simulated Annealing
5)	Tabu Search
6)	Tabu Search
7)	Variable Neighborhood Search
8)	Midterm
9)	Genetic Algorithm
10)	Genetic Algorithm
11)	Swarm Intelligence
12)	Swarm Intelligence
13)	Project Presentations
14)	Project Presentations

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:	Metaheuristics by El-Ghazali Talbi, Wiley.
References:	Modern Heuristic Optimization Techniques by Lee and El-Sharkawi, IEEE Press Handbook of Metaheuristics by Glover and Kochenberger, Klower Academic Pub

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	1	2	3	4	5
Program Öğrenme Çıktıları (PÖÇ)
1) Knowledge in mathematics, natural sciences, basic engineering, computer-based computation, and computer engineering–specific subjects; and the ability to use this knowledge in solving complex engineering problems.
2) Ability to identify, formulate, and analyze complex engineering problems by applying knowledge of basic sciences, mathematics, and engineering, while taking into account the relevant UN Sustainable Development Goals.
3) Ability to design creative solutions to complex engineering problems; ability to design complex systems, processes, devices, or products in a way that meets present and future needs, while considering realistic constraints and conditions.
4) Ability to select and use appropriate techniques, resources, and modern engineering and informatics tools—including prediction and modeling—for the analysis and solution of complex engineering problems, with an awareness of their limitations.
5) Ability to use research methods—including literature review, experimental design, experimentation, data collection, analysis, and interpretation of results—for the investigation of complex engineering problems.
6) Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7) Knowledge of ethical responsibility and conduct in accordance with the principles of the engineering profession; awareness of acting impartially, without discrimination, and embracing diversity.
8) Ability to work effectively, individually and as a member or leader of intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid).
9) Ability to communicate effectively on technical subjects, orally and in writing, by taking into account the diverse characteristics of the target audience (such as education, language, and profession).
10) Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11) An ability to engage in lifelong learning, including independent and continuous learning, to adapt to new and emerging technologies, and to critically evaluate technological changes.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Knowledge in mathematics, natural sciences, basic engineering, computer-based computation, and computer engineering–specific subjects; and the ability to use this knowledge in solving complex engineering problems.
2)	Ability to identify, formulate, and analyze complex engineering problems by applying knowledge of basic sciences, mathematics, and engineering, while taking into account the relevant UN Sustainable Development Goals.
3)	Ability to design creative solutions to complex engineering problems; ability to design complex systems, processes, devices, or products in a way that meets present and future needs, while considering realistic constraints and conditions.
4)	Ability to select and use appropriate techniques, resources, and modern engineering and informatics tools—including prediction and modeling—for the analysis and solution of complex engineering problems, with an awareness of their limitations.
5)	Ability to use research methods—including literature review, experimental design, experimentation, data collection, analysis, and interpretation of results—for the investigation of complex engineering problems.
6)	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7)	Knowledge of ethical responsibility and conduct in accordance with the principles of the engineering profession; awareness of acting impartially, without discrimination, and embracing diversity.
8)	Ability to work effectively, individually and as a member or leader of intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid).
9)	Ability to communicate effectively on technical subjects, orally and in writing, by taking into account the diverse characteristics of the target audience (such as education, language, and profession).
10)	Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11)	An ability to engage in lifelong learning, including independent and continuous learning, to adapt to new and emerging technologies, and to critically evaluate technological changes.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Case Study
Problem Solving
Demonstration
Project Preparation
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Presentation
Final Exam

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Project	1	% 25.00
Midterms	1	% 20.00
Semester Final Exam	1	% 50.00
Active Participation in Class	1	% 5.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE