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) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Discussion, Question and Answer, Problem Solving. |
| Course Lecturer(s) |
|
| 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;
|
| 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 |
Sources
| 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 | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| 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 | Gains the ability to apply the acquired knowledge in mathematics, science and engineering. | |||||
| 2 | Gains the ability to identify, formulate and solve complex engineering problems | X | ||||
| 3 | Gains the ability to accomplish the integration of systems. | |||||
| 4 | Gains the ability to design, develop, implement and improve complex systems, components, or processes. | X | ||||
| 5 | Acquires the ability to select,develop and use suitable modern engineering techniques and tools. | |||||
| 6 | Gains the ability to design/conduct experiments and collect, analyze, and interpret data. | |||||
| 7 | Gains the ability to function independently and in teams. | |||||
| 8 | Gains the ability to make use of oral and written communication skills effectively. | |||||
| 9 | Gains the ability to recognize the need for and engage in life-long learning. | |||||
| 10 | Attains the ability to understand and exercise professional and ethical responsibility. | |||||
| 11 | Gains the ability to understand the impact of engineering solutions. | |||||
| 12 | Cultivates the ability to have knowledge of contemporary issues. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 5 | 5 |
| Report | |||
| 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 | ||