Fiche détaillée d'un cours


 


Voir la fiche établissement

OPTIMIZATION TECHNIQUES

2017-2018

IESEG School of Management ( IÉSEG )

Code Cours :

1718-IÉSEG-MBD1S2-QMS-MBDCI04UE

QUANTITATIVE METHODS


Niveau Année de formation Période Langue d'enseignement 
MSc in Big Data Analytics for Business1S2English
Professeur(s) responsable(s)J.SIANI
Intervenant(s) Stefano NASINI


Pré requis

This is a mathematically and computationally oriented course, where students are expected to have previously completed basic courses in Differential and Integral Calculus, Linear Algebra, and Computer Programming.

Objectifs du cours

At the end of the course, the student should be able to:
• Understand the different mathematical programming modeling strategies (linear, nonlinear, integer)
• Design mathematical programming models for answering business problems in supply chain management, logistic, transportation, portfolio selection, etc.given a number of constraints
• Understand the different algorithmic methods for linear, nonlinear and integer optimization
• Solve linear, nonlinear and integer optimization using specialized softwares.

Contenu du cours

This is a graduate course in Optimization, which is designed to enable students to correctly model and solve linear, nonlinear and integer optimization problems. The first part of the course is oriented to the analysis of the different mathematical programming modeling strategies. The second part of the course focuses on algorithms and provides students with a collection of computational tools to correctly solve the designed models. The course is based on the use of several computational methods. Optimization software, such as AMPL, R and MINOS are presented.


Modalités d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Face to face
lecture10,00  
Interactive class6,00  
Independent study
Estimated personal workload10,00  
Group Project8,00   Students are assigned to small groups
Independent work
Reference manual 's readings8,00   From the list of recomended reading
E-Learning8,00  
Charge de travail globale de l'étudiant50,00  

Méthodes pédagogiques

  • E-learning
  • Research
  • Interactive class
  • Coaching


Évaluation

The students evaluation is based on an individual assignment and a group project.

Type de ContrôleDuréeNombrePondération
Continuous assessment
Participation16,00110,00
Others
Group Project8,00140,00
Individual Project8,00150,00
TOTAL     100,00

Bibliographie

  • D. G. Luenberger and Y. Ye, LINEAR AND NONLINEAR PROGRAMMING -




 
* Informations non contractuelles et pouvant être soumises à modification
 
 
Vidéo : Un campus à vivre
Notre chaîne Youtube