| MQGE0002-3 | ||||||||
| Computational Optimization | ||||||||
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Duration :
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| 30h Th | ||||||||
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Number of credits :
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Lecturer :
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| Yves Crama | ||||||||
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Language(s) of instruction :
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| English language | ||||||||
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Organisation and examination :
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| Teaching in the second semester | ||||||||
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Units courses prerequisite and corequisite :
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| Prerequisite or corequisite units are presented within each program | ||||||||
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Learning unit contents :
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| The aim of this course is to present various aspects of mathematical modeling and of problem-solving strategies as they are used in operations research for the solution of realistic, large-scale, complex problems.
The course contains several independent parts: - General-purpose heuristic strategies for the solution of combinatorial optimization problems, such as simulated annealing, tabu search or genetic algorithms; the practical implementation of such methods is illustrated on a variety of optimization problems. - Integer programming and network problems. Branch-and-bound method. Modeling and solution of large-scale models. |
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Learning outcomes of the learning unit :
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| By the end of this course, the students will be able to model complex decision-making problems and to implement appropriate methods for their solution. They will better understand the opportunities offered by optimization methods, as well as their intrinsic limitations.
As a side-benefit, they will also develop advanced computer programming skills that are transferable to different business contexts. Intended Learning Outcomes addressed by the course:
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Prerequisite knowledge and skills :
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| Prerequisites:
- Mathematics: calculus and matrix algebra. - Operations research: an introductory course covering linear programming models and methods. - General proficiency with personal computers. Command of a computer programming language (MathLab, SciLab, Pascal, C, Visual Basic,...) is an asset, but is not a strict prerequisite. An introduction to the language "Julia" will be provided, and the students are encouraged to use it for the development of their projects. |
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Planned learning activities and teaching methods :
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| Group and individual projects: computer implementations, written reports and oral presentations. | ||||||||
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Mode of delivery (face-to-face ; distance-learning) :
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| Lectures and computer labs. Group and individual projects: computer implementations, reports and presentations. Attendance is mandatory. | ||||||||
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Recommended or required readings :
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| Lecture notes: Y. Crama, Computational Optimization, ULg, 2016. | ||||||||
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Assessment methods and criteria :
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The final note will be based on:
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Work placement(s) :
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Organizational remarks :
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| This course is taught in English.
See the Lola Web site http://lola.hec.ulg.ac.be/index.php for additional information. |
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Contacts :
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| Y. CRAMA
(y.crama@ulg.ac.be(yasemin.arda@ulg.ac.be)
) Teaching assistant: E. RODRIGUEZ-HECK (elisabeth.rodriguezheck@ulg.ac.be) |
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