Instıtute Of Graduate Educatıon
Industrıal Engıneerıng Master's Program (Wıth Thesıs)
Course Information
HEURISTIC OPTIMIZATION | |||||
---|---|---|---|---|---|
Code | Semester | Theoretical | Practice | National Credit | ECTS Credit |
Hour / Week | |||||
INE534 | Spring | 3 | 0 | 3 | 7 |
Prerequisites and co-requisites | |
---|---|
Language of instruction | Turkish |
Type | Elective |
Level of Course | Master's |
Lecturer | |
Mode of Delivery | Face to Face |
Suggested Subject | |
Professional practise ( internship ) | None |
Objectives of the Course | As per the learning paradigm, after successfully completing this class, students will be able to: How and why heuristics and metaheuristics teckniques work. Under what circumstances metaheuristics should be used Advantages and disadvantages of heuristics and metaheuristics over other(deterministic..) methodologies. |
Contents of the Course | Available and newly introduced heauristic methods to solve/optimize combinatirial problems. Objective, abilities and practical applications of heuristic methods in optimization theory. |
Learning Outcomes of Course
# | Learning Outcomes |
---|---|
1 | Students will use the knowledge on simulating annealing, genetic algorithms, TABU search and other heuristic methodologies. |
2 | Student will be able to model,apply and analyse heuristic methods |
3 | Student will be able to apply neural networks and some other important heuristic methods. |
4 | Student will be able to analyse the results that s/he gained by applying heauristic methods, and compare with deterministic (exact) solution methodologies. |
Course Syllabus
# | Subjects | Teaching Methods and Technics |
---|---|---|
1 | Introduction to exponential complexity and algorithmic combinatorial problems | Lecturing |
2 | Branch and Bound Algorithm | Lecturing |
3 | Dominancy, bound relaxation and integer programming | Lecturing |
4 | Lagrangean Relaxation | Lecturing |
5 | Lagrangean Relaxation | Lecturing |
6 | Mİdterm | Exam |
7 | Neighborhood searching: Local and global optimality, fixing heuristics | Lecturing |
8 | Simulating annealing, general approach | Lecturing |
9 | Genetic Algorithms: populations, generation and crossover | Lecturing |
10 | Mutation, genetic modeling | Lecturing |
11 | TABU Search: Short time memory, goal, strenghting, diversification | Lecturing |
12 | Other methodologies: neual networks, hybrid techniques | Lecturing |
13 | Deluge algorithm | Lecturing |
14 | General Application | Lecturing |
15 | General Application | Lecturing |
16 | Final Exam | Exam |
Course Syllabus
# | Material / Resources | Information About Resources | Reference / Recommended Resources |
---|---|---|---|
1 | J. S. Arora, Introduction to Optimum Design, Elsevier Academic Pres, 2004. | ||
2 | G. N. Vanderplaats, Numerical Optimization Techniques for Engineering Design, McGraw-Hill, New York, 1984. | ||
3 | D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989. |
Method of Assessment
# | Weight | Work Type | Work Title |
---|---|---|---|
1 | 40% | Mid-Term Exam | Mid-Term Exam |
2 | 60% | Final Exam | Final Exam |
Relationship between Learning Outcomes of Course and Program Outcomes
# | Learning Outcomes | Program Outcomes | Method of Assessment |
---|---|---|---|
1 | Students will use the knowledge on simulating annealing, genetic algorithms, TABU search and other heuristic methodologies. | 1͵2͵3 | 1͵2 |
2 | Student will be able to model,apply and analyse heuristic methods | 2͵3͵4 | 1͵2 |
3 | Student will be able to apply neural networks and some other important heuristic methods. | 1͵2 | 1͵2 |
4 | Student will be able to analyse the results that s/he gained by applying heauristic methods, and compare with deterministic (exact) solution methodologies. | 2͵3 | 1͵2 |
Work Load Details
# | Type of Work | Quantity | Time (Hour) | Work Load |
---|---|---|---|---|
1 | Course Duration | 14 | 3 | 42 |
2 | Course Duration Except Class (Preliminary Study, Enhancement) | 14 | 4 | 56 |
3 | Presentation and Seminar Preparation | 14 | 1 | 14 |
4 | Web Research, Library and Archival Work | 14 | 3 | 42 |
5 | Document/Information Listing | 1 | 6 | 6 |
6 | Workshop | 0 | 0 | 0 |
7 | Preparation for Midterm Exam | 0 | 0 | 0 |
8 | Midterm Exam | 0 | 0 | 0 |
9 | Quiz | 0 | 0 | 0 |
10 | Homework | 0 | 0 | 0 |
11 | Midterm Project | 0 | 0 | 0 |
12 | Midterm Exercise | 0 | 0 | 0 |
13 | Final Project | 1 | 20 | 20 |
14 | Final Exercise | 0 | 0 | 0 |
15 | Preparation for Final Exam | 0 | 0 | 0 |
16 | Final Exam | 0 | 0 | 0 |
180 |