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 | ||||