| MQGE0001-6 | ||||||||||||||
| Operations Research | ||||||||||||||
|
Durée :
|
||||||||||||||
| 45h Th | ||||||||||||||
|
Nombre de crédits :
|
||||||||||||||
|
||||||||||||||
|
Nom du professeur :
|
||||||||||||||
| Yves Crama | ||||||||||||||
|
Langue(s) du cours :
|
||||||||||||||
| Langue anglaise | ||||||||||||||
|
Organisation et évaluation :
|
||||||||||||||
| Enseignement au premier quadrimestre, examen en janvier | ||||||||||||||
|
Unités d'enseignement prérequises et corequises :
|
||||||||||||||
| Les unités prérequises ou corequises sont présentées au sein de chaque programme | ||||||||||||||
|
Contenus du cours :
|
||||||||||||||
| Operations research relies on mathematical modeling to formulate and to analyze complex decision problems faced by individuals or organizations. OR models and techniques are found at the core of numerous IT tools used in everyday life and in managerial decision support systems, e.g., GPS-based routing systems, airline reservation and pricing software, production planning and scheduling systems, financial investment optimization systems, etc. In order to make a sensible use of OR tools, the manager needs to understand their full potential as well as their limitations. By getting the most out of these tools, companies may be able to develop innovative solutions and to improve their competitive position.
As such, the course directly relates to one of the main intended learning outcomes of the bachelor's programme in business engineering, namely, the ability to use analytical and IT tools in order to address management problems. The course offers an introduction to some of the most successful models and techniques used in operations research:
The course is illustrated by numerous examples and applications from logistics, production management, finance, arising in various profit or non-profit service industries. |
||||||||||||||
|
Acquis d'apprentissage (objectifs d'apprentissage) du cours :
|
||||||||||||||
Intended Learning Outcomes addressed by the course:
P2. Be able to reconstruct and to interpret the output of simple models. P3. Be able to recognize situations where OR techniques can be successfully applied and to formulate simple models. C4. Gain some understanding of the inherent limitations of the mathematical modeling approach. |
||||||||||||||
|
Savoirs et compétences prérequis :
|
||||||||||||||
| Mathematics (linear algebra, matrix algebra), probability theory and statistics (concept of random variable, classical distributions, expectation, variance, etc.). | ||||||||||||||
|
Activités d'apprentissage prévues et méthodes d'enseignement :
|
||||||||||||||
| A1. Lectures.
A1. Readings (lecture notes and case studies). A2. Numerical exercises. A2. Computer labs. A3. Computer-based project: formulation and solution of a small case. |
||||||||||||||
|
Mode d'enseignement (présentiel ; enseignement à distance) :
|
||||||||||||||
| Face-to-face lectures and practice sessions. | ||||||||||||||
|
Lectures recommandées ou obligatoires et notes de cours :
|
||||||||||||||
| Lecture notes:
Y. Crama, Operations Research, ULg, 2013.
Additional material to be found on the virtual campus Lol@:
|
||||||||||||||
|
Modalités d'évaluation et critères :
|
||||||||||||||
| Based on: - written examination in January (90%). - modeling project (10%). | ||||||||||||||
|
Stage(s) :
|
||||||||||||||
|
Remarques organisationnelles :
|
||||||||||||||
| The course is taught in English . | ||||||||||||||
|
Contacts :
|
||||||||||||||
| Instructor: Prof. Y. CRAMA HEC-Management School (Building N1) Email : Y.Crama@ulg.ac.be Teaching assistant: R. SADRABADI HEC-Management School (Building N1) Room 328 Email: M.Rezaei@ulg.ac.be (%20m.rezaei@ulg.ac.be) | ||||||||||||||