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Data Mining and Optimisation

Objectives


Enterprises and companies involved in production or services activities management have to deal with NP-hard decision and/or optimization problems (from a computational complexity point of view) : scheduling, transportation, logistics, resource allocation, air traffic management ...
Solving such problems leads to compute one (or several) solution(s) with respect to the constraints while having a good quality relatively to an objective function.
The first part of this course aims to present a general modelling framework, (Constraints Satisfaction Problems, so called CSPs), and to give understandings of constraints propagation mechanisms and algorithms, and finally programming languages or libraries that embed them.
Lab works are dedicated to constraint logic programming with ECLiPSe (a Prolog extension).
The second part gives some concepts, methods and algorithms in combinatorial optimization and presents some exact and approximate methods.
The project concerns practical applications (such as transportation or scheduling problems).

Needed prerequisite


Data Structures, Algorithmics, Complexity,
Graph Theory,
First-Order Logic and Logic Programming,
Introduction to Operations Research,
Introduction to Intelligence Artificial (decision problems).

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...