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Optimization with Metaheuristics

Exam number: 6778

Semester: from 2nd semester

Duration of the module: One semester

Form of the module (i.e. obligatory, elective etc.): Elective

Frequency of module offer: Winter semester 2016/2017

Prerequisites: None. Maximum 20 participants. More information regarding the organization and the registration can be found on the Chair’s website.

Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.

Person responsible for module: Prof. Dr. Christian Almeder

Name of the professor: Prof. Dr. Christian Almeder

Language of teaching: English

ECTS-Credits (based on the workload): 6

Workload and its composition (self-study, contact time):
Contact time (Lecture, tutorial etc.): 45 h; self-study: 135 h

Contact hours (per week in semester): 3

Methods and duration of examination:
Final Exam (90min) 70%; 2 Assignments á 5%; 1 Assignment+Presentation á 20%

Emphasis of the grade for the final grade: Please check regulations of the study programme

Aim of the module (expected learning outcomes and competencies to be acquired):
Many planning problems in operations management are difficult to solve under strict time constraints. Metaheuristic methods are state-of-the-art optimization techniques, which allow a fast planning also under difficult real-world constraints and time restrictions. Aim of the course is to learn and understand the basic concepts of metaheuristics and its implementation in a high-level programming language (Python).

Contents of the module:
Introduction to programming with Python
Local Search Techniques
Randomized algorithms and variable acceptance criteria
Simulated Annealing
Variable neighborhood Search
Tabu Search

Teaching and learning methods:
Lectures, exercise

Special features (e.g. percentage of online-work, practice, guest speaker, etc.):
Lecture notes and additional material are provided online.

Literature (compulsory reading, recommended literature):
Swaroop, C. H.: A Byte of Python. Enllaç web (2003).
Zelle, John M. Python programming: an introduction to computer science. 2nd edition. Franklin, Beedle & Associates, Inc., 2009.
Gendreau, Michel, and Jean-Yves Potvin. Handbook of metaheuristics. Vol. 2. New York: Springer, 2010.

Further information:
Registration in Moodle Viadrina required.