ADVANCED OPTIMIZATION METHODS

Code Cours
2324-IÉSEG-BA3S2-QMS-B3-CE09UE
Language of instruction
English
Teaching content
QUANTITATIVE METHODS
This course occurs in the following program(s)
Training officer(s)
M.BUISINE
Stakeholder(s)
Joseph Sian
, Matthieu Buisine
Level
Bachelor
Program year
Period

Présentation

Prerequisite
- Being able to model single objective problems
- Mastering the solving methods (graphical, spreadsheet, simplex, Big M…)
- Being able to conduct a sensibility analysis.
Goal
- Take optimal decisions in the presence of trade-offs between two or more conflicting objectives.
- Be able to solve a transportation problem
- maximize performance whilst minimizing other counstaints at the same time.
- Find a representative set of Pareto optimal solutions, and/or quantify the trade-offs in satisfying the different objectives and/or finding a single solution that satisfies the subjective preferences of a human decision maker
Presentation
- Mathematical foundations of optimization, modeling and solution techniques for very large and/or complex problems, algorithmic solution methods.
- Duality and transportation problem
- Network optimisation
- Integer programming
- Decision under risk and uncertainty
- Markov chains

Modalités

Organization
Type Amount of time Comment
Présentiel
Cours interactif 16,00
Autoformation
Lecture du manuel de référence 6,00
Travail personnel
Group Project 10,00
Charge de travail personnel indicative 15,00
Overall student workload 47,00
Evaluation
In this course, the student will be evaluated by:
• a classical final exam
• involvement in the final project (individual work)
• project results (collective work
Control type Duration Amount Weighting
Contrôle continu
Participation 16,00 1 25,00
Examen (final)
Examen écrit 2,00 1 40,00
Autres
Projet Collectif 0,00 1 35,00
TOTAL 100,00

Ressources

Bibliography
Operations Research, Applications and Algorithms (2004) Winston, Brooks/Cole -
Internet resources