PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ENM 507	HEURISTIC METHODS AND APPLICATIONS	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	A large part of the research area of industrial engineering includes NP-hard problems. These problems usually can not be solved by exact optimization techniques. In recent years, heuristic techniques will be effectively deal with these problems. In this course, heuristic techniques and its application areas will be introduced.
Course Content	Introduction to Optimization problems, NP-Complete problems, Lagrangean Relaxation and Lagrangean Heuristics, Classical Construction Heuristics (Savings, Nearest Neighbor, Greedy) Classical Improvement Heuristics (Node Insertion, k-opt, or-opt), Meta-heuristic Methods (Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony)
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Student learns the basic concepts of heuristic methods
2	Student gains the ability of identificating problems and finding solutions by using a mathematical model.
3	Student gains the ability of improving classical and heuristic methods for the solution of NP-Hard problems.

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10
LO 001	3	3	1	4	1	1	1	1	1	2
LO 002	5	5	4	5	3	1	1	1	1	3
LO 003	5	5	5	4	3	2	1	1	1	3
Sub Total	13	13	10	13	7	4	3	3	3	8
Contribution	4	4	3	4	2	1	1	1	1	3

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Assignments	4	5	20
Mid-terms	1	60	60
Final examination	1	73	73
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2023-2024 Fall	1	CAN BERK KALAYCI

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
ENM 507	HEURISTIC METHODS AND APPLICATIONS	3 + 0	1	Turkish	2023-2024 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. CAN BERK KALAYCI	cbkalayci@pau.edu.tr		MUH A0457	%70

Goals		A large part of the research area of industrial engineering includes NP-hard problems. These problems usually can not be solved by exact optimization techniques. In recent years, heuristic techniques will be effectively deal with these problems. In this course, heuristic techniques and its application areas will be introduced.
Content		Introduction to Optimization problems, NP-Complete problems, Lagrangean Relaxation and Lagrangean Heuristics, Classical Construction Heuristics (Savings, Nearest Neighbor, Greedy) Classical Improvement Heuristics (Node Insertion, k-opt, or-opt), Meta-heuristic Methods (Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony)

Topics

Weeks	Topics
1	Introduction
2	Introduction to Optimization Problems
3	Simulated Annealing
4	Tabu Search
5	Genetic Algorithms
6	Variable Neighborhood Search
7	Ant colony Algorithm
8	Midterm exam
9	Artificial Immune System Algorithm
10	Differential Evolution Algorithm
11	Particle Swarm Optimization
12	Artificial Bee Colony Algorithm
13	Artificial Neural Networks and other heuristic approaches
14	Presentation of projects

Materials

Materials are not specified.

Resources

Resources	Resources Language
1. Yapay Zeka Optimisazyon Algoritmaları, Derviş Karaboğa, Nobel Yayın Dağıtım, 2011.	Türkçe
2. Modern Sezgisel Teknikler ve Uygulamaları, Dr. Tunçhan Cura, Papatya Yayıncılık Eğitim 2008.	Türkçe
3. Handbook of Metaheuristics (International Series in Operations Research & Management Science), Michel Genderau, Jean-Yves Potvin, Springer, 2012.	Türkçe
4. Modern Heuristic Search Methods, V. J. Rayward-Smith, I. H. Osman, C. R. Reeves, G. D. Smith, Wiley, 1996.	Türkçe
5. Metaheuristics for Hard Optimization, J. Dreo, P. Siarry, A. Petrowski, E. Taillard, Springer, 2003.	Türkçe

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student