PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
IENG 479	HEURISTIC METHODS AND APPLICATIONS	3 + 0	8th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor'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

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
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Mid-terms	1	20	20
Final examination	1	26	26
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
IENG 479	HEURISTIC METHODS AND APPLICATIONS	3 + 0	1	Turkish	2023-2024 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. KENAN KARAGÜL	kkaragul@pau.edu.tr		MUH A0435	%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 To Metaheuristic Algorithms
2	Local and Global Search Algorithms
3	Trajectory Based Search Algorithms
4	Population Based Metaheuristics
5	Evolution Strategies
6	Evolutionary Programming and Genetic Programming
7	Bio-inspired Metaheuristics
8	Other population-based metaheuristics , Scalability of Population-based Metaheuristics
9	Midterm
10	Evolutionary Multiobjective Optimization
11	Feedforward Neural Networks
12	Neural Networks with Unsupervised Learning
13	Recurrent Neural Networks
14	Evolutionary Design of Neural Networks

Materials

Materials are not specified.

Resources

Resources	Resources Language
Essentials of Metaheuristics, Sean Luke, Second Edtion, Online Version, October, 2015	English
Clever Algorithms, Jason Brownlee, website online at http://www.CleverAlgorithms.com	English
Yazılım Geliştirme Aracı olarak açık kaynak kodlu SciLab : http://www.scilab.org/	English

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	60	Final Exam
Midterm Exam	40	Midterm Exam

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

Prospective Student