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GIMAS8AE

Advanced Discrete OptimisationOptimization

ECTS Credits: 2

Semester: S8

Person(s) in charge:

Bernadetta Bernardetta ADDIS, Lecturer, bernadetta.addis@mines-nancy.univ-lorraine.fr

Keywords: Discrete optimisationoptimization

Prerequisites: course SG134: Discrete Optimization

Objective:

Advanced techniques in discrete optimisationoptimization

Program and contents

Objectives

First, we present different approaches to constructing exact and approximated methods for difficult optimisation problems. These approaches will be illustrated on different examples already modeled in the course SG 134. The approximation methods can be constructed with guaranteed performance in comparison with the optimal solution. The second part of this course will be dedicated to generic methods for combinatorial problems such as meta-heuristics, evolutionist algorithms (genetic algorithms) as well as constraint programming.  In last part, we present other analysis techniques when we are in the presence of problems with several criteria.

Content 

•  Exact Methods such as branch and bound
• Relaxation and approximation of optimisation optimization problems
• Meta-heuristics and genetic algorithms
• Constraint programming
• Multi-criteria analysis of optimisation optimization problems.

Abilities: 

Levels

Description and operational verbs

Know 

Understand 

Apply 

Analyze 

Summarise

Assess

Evaluation:

•  Written test

•  Continuous Control

•  Oral report

•  Project

•  Written report