Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
9204045442017Heuristic MethodsElective124
Level of Course Unit
Second Cycle
Objectives of the Course
A large part of the research area of industrial engineering includes NP-hard problems. These problems usually cannot 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.
Name of Lecturer(s)
DR. ÖĞR. ÜYESİ U. GÖKAY ÇİÇEKLİ
Learning Outcomes
1Student learns the basic concepts of heuristic methods
2Student gains the ability of identifying problems and finding solutions by using a mathematical model.
3Student gains the ability of improving classical and heuristic methods for the solution of NP-Hard problems.
Mode of Delivery
Evening Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
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) will be introduced.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Introduction to Optimization Problems
2NP-Complete problems
3Lagrange multipliers and heuristics
4Classical Heuristic Methods (Savings)
5Classical Heuristic Methods (Nearest Neighbor)
6Classical Heuristic Methods (Greedy)
7Improving heuristics (adding node, k-opt, or-opt)
8Midterm Exam
9Improving heuristics (adding node, k-opt, or-opt)
10Parametric Heuristic Methods
11Parametric Heuristic Methods
12Project Presentations
13Project Presentations
14Project Presentations
15Project Presentations
16Final Exam
Recommended or Required Reading
• Wayne L. Winston (2003) Operations Research: Applications and Algorithms, Yayınevi: Cengage: Brooks / Cole. • All related articles
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
Turkish
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Attending Lectures16348
Seminar11515
Self Study11515
Individual Study for Mid term Examination11515
Individual Study for Final Examination11515
Reading188
TOTAL WORKLOAD (hours)120
Contribution of Learning Outcomes to Programme Outcomes
PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
PO
7
PO
8
PO
9
PO
10
LO15454554545
LO25454554545
LO35454554545
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr