Department of Industrial Engineering

IE 358 | Course Introduction and Application Information

Course Name

Heuristics in Optimization

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

Prerequisites	IE 251 To be successful (To have received at least DD grade)
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	Lecture/Presentation
National Occupational Classification Code	-
Course Coordinator	Doç. Dr. Ömer Özkan
Course Lecturer(s)	Doç. Dr. Ömer Özkan
Assistant(s)	-

Course Objectives

The aim of this course is to teach the fundamental principles of metaheuristic algorithms and demonstrate how these algorithms can be applied to various optimization problems.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Students will be able to describe the role of heuristic methods in optimization.				X
LO2	Students will be able to explain the structures and operating principles of fundamental heuristic methods such as simulated annealing, particle swarm optimization, genetic algorithms, ant colony optimization, and tabu search algorithms.						X
LO3	Students will be able to compare the Greedy Randomized Adaptive Search Procedure (GRASP) and Scatter Search methods in optimization problems.				X
LO4	Students will be able to define local search methods and neighborhood structures in optimization problems.					X

Course Description

Introduction to Complexity and Heuristics; Simulated Annealing; Particle Swarm Optimization; Genetic Algorithms and Evolutionary Strategies; Ant Colony Optimization; Tabu Search Algorithm; Greedy Randomized Adaptive Search Procedure (GRASP); Scatter Search Algorithm; Local Search and Neighborhood Structures

Related Sustainable Development Goals

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Review of Operations Research Topics	Michalewicz, Z. (2013), Chp1	LO1
2	Introduction to Complexity and Heuristics	Lecture Notes	LO1
3	Simulated Annealing	Lecture Notes Michalewicz, Z. (2013), Chp5 Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp10	LO2
4	Particle Swarm Optimization	Lecture Notes	LO2
5	Genetic Algorithms and Evolutionary Strategies	Lecture Notes Michalewicz, Z. (2013), Chp6 Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp3	LO2
6	Genetic Algorithms and Evolutionary Strategies	Lecture Notes Michalewicz, Z. (2013), Chp6 Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp3	LO2
7	Ant Colony Optimization	Lecture Notes Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp9	LO2
8	Midterm Exam		-
9	Tabu Search Algortihm	Lecture Notes Michalewicz, Z. (2013), Chp5 Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp2	LO2
10	Tabu Search Algortihm	Lecture Notes Michalewicz, Z. (2013), Chp5 Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp2	LO2
11	Greedy Randomized Adaptive Search Procedure (GRASP) algorithm	Lecture Notes Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp8	LO3
12	Scatter Search Algorithm	Lecture Notes Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp1	LO3
13	Local Search and Neighborhoods	Lecture Notes Michalewicz, Z. (2013), Chp3 Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp10	LO4
14	Local Search and Neighborhoods	Lecture Notes Michalewicz, Z. (2013), Chp3 Glover, F. W., & Kochenberger, G. A. (Eds.). (2003) Chp10	LO4
15	Review		-
16	Final exam		-

Course Notes/Textbooks	Michalewicz Z (2013) How to Solve it Modern Heuristics Springer Science and Business Media
Suggested Readings/Materials	Lecture Notes Glover F W Kochenberger G A (Eds) (2003) Handbook of metaheuristics (Vol 57) Springer Science and Business Media

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4
Homework / Assignments	2	25	X	X	X	X
Project	1	35	X	X	X	X
Midterm	1	20	X	X
Final Exam	1	20	X	X	X	X
Total	5	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	5	70
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	2	5	10
Presentation / Jury	-	-	-
Project	1	22	22
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	10	10
Final Exam	1	20	20
		Total	180