Quantitative Decision Making

Teacher(s)

Catanzaro Daniele;

Language

English

Prerequisites

• MQANT1110 - Mathématiques de gestion 1
• MQANT1227 - Mathématiques de gestion 2
• MQANT1329 - Optimisation
• MQANT1223 - Informatique et algorithmique
• MINFO1302 - Projet de Programmation

Main themes

This course is designed to develop in the student both the ability to quantitatively analyze practical problems and to interpret and understand quantitative results in order to perform a more informed decision-making. Its aim is to introduce a broad range of optimization concepts and associated quantitative techniques with a view to helping the student appreciate the merits and limitations of these techniques as well as the data and technical requirements involved with their use. 

Learning outcomes

At the end of this learning unit, the student is able to :
1	This course contributes to develop the following competencies. Knowledge Scientific reasoning and systematic approach Communication and interpersonal skills Project management Leadership At the end of this course, students will: Improve their strategical thinking skills Acquire fundamental knowledge on the modeling of practical problems Apply the appropriate techniques to propose a useful solution.

Content

This course is designed to develop both the ability to quantitatively analyze very large-scale practical problems in management science and to interpret and understand quantitative results in order to perform a more informed decision-making. Its aim is to introduce a broad range of optimization concepts and associated quantitative techniques with a view to helping the student appreciate the merits and limitations of these techniques as well as the data and technical requirements involved with their use.
The course includes the following topics:

1. Introduction to Quantitative Decision Making Tools
2. Large Scale Optimization: From Theory to Solutions
3. Projection, inverse projection, and their applications
4. Models and methods for Data Envelopment Analysis, Pricing, Location, Partitioning, Routing, Transportation and Network Design
5. Case studies
6. Brief introduction to integer optimization methods for machine learning 

Teaching methods

Slided & Blackboard lectures.

Evaluation methods

The examination method (e.g., project, written exam, or other forms) will be communicated by the lecturer during the first and *madatory* lecture of the course. 

Bibliography

The lectures will be integrated with some capita selecta from the following references: (1) R. Kipp Martin. Large Scale Linear and Integer Optimization: A Unified Approach. Springer, 1999. (1) S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press 2004. (2) M. Conforti, G. Cornuejols, G. Zambelli. Integer Programming. Springer, 2014. (3) S. Heipcke. Applications of optimization with Xpress-MP. Dash Optimization, 2002.

Faculty or entity

CLSM