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 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
IE 302, IE 304
Course Language English
Course Type Elective Courses
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 Acquires sufficient knowledge in mathematics, natural sciences, and related engineering disciplines; gains the ability to use theoretical and applied knowledge in these fields in solving complex engineering problems.
2 Gains the ability to identify, define, formulate, and solve complex engineering problems; acquires the skill to select and apply appropriate analysis and modeling methods for this purpose. X
3 Gains the ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions, and applies modern design methods for this purpose. X
4 Develops the skills to develop, select, and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; gains the ability to effectively use information technologies.
5 Gains the ability to design experiments, conduct experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics.
6 Acquires the ability to work effectively in intra-disciplinary and multidisciplinary teams, as well as individual work skills.
7 Acquires effective oral and written communication skills in Turkish; at least one foreign language proficiency; gains the ability to write effective reports, understand written reports, prepare design and production reports, make effective presentations, and give and receive clear instructions.
8 Develops awareness of the necessity of lifelong learning; gains the ability to access information, follow developments in science and technology, and continuously renew oneself.
9 Acquires the consciousness of adhering to ethical principles, and gains professional and ethical responsibility awareness. Gains knowledge about the standards used in industrial engineering applications.
10 Gains knowledge about practices in the business life such as project management, risk management, and change management. Develops awareness about entrepreneurship and innovation. Gains knowledge about sustainable development.
11 Gains knowledge about the universal and social dimensions of the impacts of industrial engineering applications on health, environment, and safety, as well as the problems reflected in the engineering field of the era. Gains awareness of the legal consequences of engineering solutions.
12 Gains skills in the design, development, implementation, and improvement of integrated systems involving human, material, information, equipment, and energy.
13 Gains knowledge about appropriate analytical and experimental methods, as well as computational methods, for ensuring 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