ECTS - Heuristic Methods for Optimization

Heuristic Methods for Optimization (IE420) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Heuristic Methods for Optimization IE420 3 0 0 3 5
Pre-requisite Course(s)
IE 302, IE 304
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives Upon successful completion of this course, students should gain knowledge of how and why heuristic techniques work, when they should be applied and their relative merits with respect to each other and with respect to more traditional approaches, such as mathematical programming.
Course Learning Outcomes The students who succeeded in this course;
  • Students will be able to acquire knowledge of some common heuristics, such as simulated annealing, genetic algorithms, and evolutionary strategies and TABU search.
  • Students will be able to analyze and model using common heuristic search methods.
  • Students will be able to demonstrate knowledge with some other heuristic methods, such as neural networks and random methods.
  • Students will be able to interpret and use the results obtained by applying heuristic methods.
Course Content Introduction of a variety of important, main-stream heuristic techniques, both traditional and modern, for solving combinatorial problems; reasons for the existence of heuristic techniques, their applicability and capabilities.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction: computational growth rate, algorithmic complexity and combinatorial problem
2 Branch-and-Bound: branching, bounding, node development
3 Dominance, relaxation to provide bounds and integer programming
4 Lagrangian relaxation method
5 Lagrangian relaxation method
6 Local search: neighborhoods, local and global optimality, constructive and improvement heuristic techniques
7 Local search: neighborhoods, local and global optimality, constructive and improvement heuristic techniques
8 Simulated annealing: general approach, cooling schedules and variants
9 Genetic algorithms: populations, reproduction, crossover
10 Midterm
11 Mutation, demes, competition and genetic programming
12 TABU search: short term memory, TABU status, aspiration, intensification and diversification
13 TABU search: short term memory, TABU status, aspiration, intensification and diversification
14 Other methods and techniques: neural networks, random methods, hybrid methods
15 Great Deluge algorithm, record-to-record transfer and parallel implementation
16 Final Examination Period


Course Book 1. Reeves, C. R., Modern Heuristic Techniques for Combinatorial Problems, John Wiley & Sons, 1993.
Other Sources 2. Sait, S.M., and Youssef, H., Iterative Algorithms with Applications in Engineering, IEEE Press, 1999.
3. Papadimitriou, C.H., and Steiglitz, K., Combinatorial Optimization: Algorithms and Complexity, Prentice-Hall, 1982.
4. Nemhauser, G.L., and Wolsey, L.A., Integer and Combinatorial Optimization, John Wiley & Sons, 1998.
5. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., and Shmoys, D.B., The Traveling Salesman Problem, John Wiley & Sons, 1985.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 15
Presentation - -
Project 1 20
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 25
Final Exam/Final Jury 1 40
Toplam 6 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

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

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge of mathematics, science and engineering to Industrial Engineering; an ability to apply theoretical and practical knowledge to model and solve engineering problems.
2 An ability to identify, formulate and solve complex engineering problems; an ability to select and apply proper analysis and modeling methods. X
3 An ability to design a complex system, process, tool or component to meet desired needs within realistic constraints; an ability to apply modern design. X
4 An ability to develop, select and put into practice techniques, skills and modern engineering tools necessary for engineering practice; an ability to use information technology effectively.
5 An ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or disciplinary research topics.
6 An ability to work individually, on teams, and/or on multidisciplinary teams.
7 Ability to communicate effectively in Turkish orally and in writing; knowledge of at least one foreign language; effective report writing and understand written reports, preparing design and production reports, making effective presentations, giving and receiving clear and understandable instruction.
8 A recognition of the need for, and an ability to engage in life-long learning; an ability to use information-seeking tools and to follow the improvements in science and technology.
9 An ability to behave according to the ethical principles, an understanding of professional and ethical responsibility. Information on standards used in industrial engineering applications.
10 Knowledge of business applications such as project management, risk management and change management. A recognition of entrepreneurship, innovativeness. Knowledge of sustainable improvement.
11 Information on the effects of industrial engineering practices on health, environment and security in universal and societal dimensions and the information on the problems of the in the field of engineering of the era. Awareness of the legal consequences of engineering solutions.
12 An ability to design, development, implementation and improvement of integrated systems that include human, materials, information, equipment and energy.
13 Knowlede on appropriate analytical, computational and experimental methods to provide system integration.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
Project 1 5 5
Homework Assignments 3 3 9
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 5 5
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 125