COURSE UNIT TITLE

: APPLIED METAHEURISTICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IND 5041 APPLIED METAHEURISTICS ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR ADIL BAYKASOĞLU

Offered to

INDUSTRIAL ENGINEERING - NON THESIS
INDUSTRIAL ENGINEERING
INDUSTRIAL ENGINEERING
INDUSTRIAL ENGINEERING - NON THESIS (EVENING PROGRAM)

Course Objective

Teaching meta-heuristic optimization techniques to students with theoretical, computer application and case studies. In this course metaheuristic optimization and its possible implementation techniques will be presented. Several meta-heuristic techniques like simulated annealing, tabu search, genetic algorithms, and scatter search will be presented through their implementations in various languages. In the course applications of these techniques will also be provided along with case studies.

Learning Outcomes of the Course Unit

1   This course is expected to help the student to learn basic principles of metaheuristic techniques
2   To enable students to solve complex engineering design and optimization problems with metaheuristics.
3   To enable students to develop computer programs to model and solve practical problems with metaheuristics.
4   To enable students to carry out research studies on metaheuristics

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction and Straightforward Optimization Methods
2 Search Heuristics
3 Metaheuristics in General
4 Metaheuristics Based on Solution Construction (Greedy Randomized Adaptive Search Procedure, Ant Colony Optimization)
5 Computer implementation (in Matlab, C or other languages)
6 Metaheuristics Based on Solution Modification (Local Search as a common principle, Tabu Search)
7 Threshold Accepting, Simulated Annealing and computer implementations
8 Metaheuristics Based on Solution Recombination (Genetic Algorithm, Scatter Search)
9 Further Metaheuristics (Variable Neighborhood Search, Particle Swarm Optimization, Bee Colony and others)
10 Midterm-1
11 Metaheuristics in Engineering Design Optimization
12 Metaheuristics in Cellular Manufacturing
13 Metaheuristics in Production Planning and Scheduling
14 Case study presentations and discussions

Recomended or Required Reading

1-Metaheuristic Search Concepts, Günther Zäpfel, Roland Braune, Michael Bögl, Springer and Verlag, Berlin Heidelberg 2010
2- Instructor's notes

Planned Learning Activities and Teaching Methods

Class presentations, case studies and practical applications

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 + ASG *0.25 +FIN *0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG *0.25 +RST *0.50


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Prof.Dr.Adil Baykasoğlu
+90 232 301 76 00

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 7 91
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparing assignments 1 20 20
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 186

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.134533
LO.24434
LO.35345
LO.43433

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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