IE 358 | Course Introduction and Application Information - Department of Mathematics

Course Name

Heuristics in Optimization

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
IE 358	Fall/Spring	3	0	3	6

Prerequisites

IE 251 To succeed (To get a grade of at least DD)

Course Language

English

Course Type

Elective

Course Level

First Cycle

Mode of Delivery

-

Teaching Methods and Techniques of the Course

Lecture / Presentation

National Occupation Classification

-

Course Coordinator

Dr. Öğr. Üyesi Kamil Erkan Kabak

Course Lecturer(s)

Dr. Öğr. Üyesi Kamil Erkan Kabak

Assistant(s)

-

Course Objectives

The purpose of this course is to fundamental concepts of heuristics in solving various optimization problems with emphasis on metaheuristics

Learning Outcomes

The students who succeeded in this course;

Will be able to list the basic heuristic methods for optimization
Will be able to compare and contrast these methods with classical optimization methods
Will be able to list basic meta-heuristic methods for optimization
Will be able to adapt these heuristic methods especially to Industrial Systems Engineering problems
Will be able to improve these heuristic methods adapted to Industrial Systems Engineering problems
Will be able to implement Improve these heuristic methods adapted to Industrial Systems Engineering problems

Course Description

This course introduces the concept of heuristics to students who already know about mathematical optimization. The topics include basic heuristic constructs (greedy, improvement, construction); meta heuristics such as simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. The basic material on the heuristic will be covered in regular lectures The students will be required to present a variety of application papers on different subjects related to the course. In addition, as a project assignment the students will design a heuristic, write a code of an appropriate algorithm for the problem and evaluate its performance.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
1	Reminder for Optimization	Reading: Textbook (Michalewicz) introduction Ch 1
2	Introduction to complexity and heuristics	Lecture notes/slides provided
3	Simulated Annealing	Lecture notes/ slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 8
4	Particle Swarm Optimization	Lecture notes/slides provided
5	Genetic Algorithms and Evolutionary Strategies 1	Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 3
6	Genetic Algorithms and Evolutionary Strategies 1	Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 3
7	MIDTERM
8	Ant Colony Optimization	Lecture notes/slides provided, Reading: Handbook of Metaheuristics Ch 9
9	Tabu Search	Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 2
10	Tabu Search	Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 2
11	GRASP	Lecture notes/slides provided Handbook of Metaheuristics Ch 8
12	Scatter Search	Lecture notes/slides provided Handbook of Metaheuristics Ch 1
13	Local Search 1	Lecture notes/slides provided Handbook of Metaheuristics Ch 11
14	Local Search 2 Neighbourhoods, VNS	Lecture notes/slides provided Reading: Handbook of MetaheuristicsCh 6
15	Review of Final	Lecture notes/slides provided
16	Review of the Semester

Course Notes/Textbooks	Textbook:Zbigniew Michalewicz, David B. Fogel “How to Solve It: Modern Heuristics
Suggested Readings/Materials	"Handbook of Metaheuristics" edt by: Glover F.,, Kochenberger G.A., Kluwer, 2003 and Lecture PowerPoint slides

Semester Activities	Number	Weigthing
Participation	1	5
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	5	20
Presentation / Jury
Project	1	35
Seminar / Workshop
Oral Exams
Midterm	1	20
Final Exam	1	20
Total

Weighting of Semester Activities on the Final Grade		80
Weighting of End-of-Semester Activities on the Final Grade		20
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	5	70
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	5	3	15
Presentation / Jury			0
Project	1	17	17
Seminar / Workshop			0
Oral Exam			0
Midterms	1	10	10
Final Exam	1	20	20
		Total	180

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.		-	-	-	-	-
2	To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.		-	-	-	-	-
3	To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.		-	-	X	-	-
4	To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.		-	-	-	-	-
5	To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.		-	-	X	-	-
6	To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.		-	-	-	-	-
7	To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.		-	-	-	-	-
8	To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.		-	-	X	-	-
9	To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.		-	-	-	-	-
10	To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.		-	-	-	X	-
11	To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.		-	-	-	-	-
12	To be able to speak a second foreign language at a medium level of fluency efficiently.		-	-	-	-	-
13	To be able to relate the knowledge accumulated throughout the human history to their field of expertise.		-	-	-	-	-