 | |
| MQGE0001-1 | Operations Research
 |
|
| Durée : | 24h Th |
|
| Crédits/ECTS : |
| Master en sciences économiques, orientation générale, à finalité approfondie, 1re année | | Toute l'année | | 5 |
|
| Master en sciences économiques, orientation générale, à finalité didactique, 1re année | | Toute l'année | | 5 |
|
| Master en ingénieur de gestion, à finalité didactique, 1re année | | Toute l'année | | 4 |
|
| Master en sciences économiques, orientation générale, à finalité spécialisée , 1re année | | Toute l'année | | 5 |
|
| Master en ingénieur de gestion, à finalité spécialisée, 1re année | | Toute l'année | | 4 |
|
|
|
| Titulaire(s) : | Yves Crama |
|
| Langue : | Langue anglaise |
|
| Aperçu général : | 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 innovative 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 potential as well as their limitations. By drawing the most out of them, companies may be able to develop innovative solutions and to improve their competitive position.
The course offers an introduction to the most successful models and techniques used in operations research:
* linear programming: modeling, simplex method, sensitivity analysis, duality;
* queuing theory: basic features, arrival and service processes, M/M/c models;
* simulation : basic principles, random generators, analysis of results.
The course is illustrated by numerous examples and applications. |
|
| Objectif du cours : | C1. Acquire some familiarity with the mathematical modeling approach to decision-making, and with fundamental models and methods used in operations research: linear programming, queueing models and simulation.
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 for the limitations inherent to the mathematical modelling approach. |
|
| Pré-requis : | Mathematics (linear algebra, matrix algebra), probability theory and statistics (concept of random variable, classical distributions, expectation, variance, etc.). |
|
| Organisation : | (Tentative plan)
A1. Lectures.
A1. Readings (lecture notes and case studies).
A2. Numerical exercises and computer labs.
A3. Mini-project (formulation and resolution of a small case; to be confirmed). |
|
| Notes de cours : | Lecture notes:
Y. Crama, Operations Research, ULg, 2007.
Additional material to be found on the virtual campus Lol@:
- articles: A Business Executive's Guide to Modern OR, OR The Productivity Engine, Queueing at Vancouver Airport, etc.
- PowerPoint slides
- exercises
|
|
| Evaluation : | Written examination during the period immediately following the end of the course.
The evaluation of the mini-project contributes -1, 0, or +1 point to the final grade. |
|
| Contacts : | Professeur: Y. CRAMA
HEC-Ecole de Gestion (Bât. B31)
Tél. : 04 366 30 77
Email : Y.Crama@ulg.ac.be
Assistante: M. BAY
HEC-Ecole de Gestion (Bât. B31)
Tél. : 04 366 31 05
Email : Maud.Bay@ulg.ac.be |
|
| Remarques : | The course is taught in English. |
|
|
| |
