REALOPT - 2017 - Annual activity report

REALOPT

REALOPT - 2017

Project-Team Realopt

Personnel

Overall Objectives

Research Program

Introduction
Polyhedral approaches for MIP
Decomposition and reformulation approaches
Integration of Artificial Intelligence Techniques in Integer Programming
Robust Optimization
Polyhedral Combinatorics and Graph Theory

Application Domains

Introduction
Network Design and Routing Problems
Packing and Covering Problems
Planning, Scheduling, and Logistic Problems
Resource Allocation for High Performance and Cloud Computing

Highlights of the Year

New Software and Platforms

BaPCod
WineryPlanning
ORTOJ

New Results

Improving Branch-and-Price Methods
Aggregation Techniques
Revisiting Benders Decomposition & Enhancing the Algorithm
Routing Problems
Machine Scheduling Problems
Scheduling Strategies for Runtime Systems
Matrix Partitioning for Parallel Computing on Heterogeneous Platforms
Convergence between HPC and Data Science
Network Design Problems
Two-dimensional Guillotine Cutting Problems
On sets avoiding distance 1
Separating Codes and Traffic Monitoring

Bilateral Contracts and Grants with Industry

Collaboration with EDF on robust maintenance planning
Collaboration with ERTUS on phytosanitary treatment planning
Collaboration with St-Gobain Recherche on glass cutting
Collaboration with SNCF on timetable and rolling stock rotation planning

Partnerships and Cooperations

National Initiatives
International Initiatives
International Research Visitors

Dissemination

Promoting Scientific Activities
Teaching - Supervision - Juries
Popularization

Bibliography

Major publications
Publications of the year
References in notes

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2017 Project-Team Activity Report

REALOPT

Reformulations based algorithms for Combinatorial Optimization

Research centre: Bordeaux - Sud-Ouest

In partnership with: CNRS, Université de Bordeaux

In collaboration with: Institut de Mathématiques de Bordeaux (IMB), Laboratoire Bordelais de Recherche en Informatique (LaBRI)

Field: Applied Mathematics, Computation and Simulation

Theme: Optimization, machine learning and statistical methods

Keywords:

Computer Science and Digital Science:

A8.1. - Discrete mathematics, combinatorics
A8.2. - Optimization
A8.7. - Graph theory

Other Research Topics and Application Domains:

B6.5. - Information systems