Production engineering

LOCO2

The LOgistique COopérative et Optimisation (LOCO2) team focuses on the modelling and optimization of production and transportation planning activities. The work is oriented towards the implementation of advanced methodologies in approximate optimization (metaheuristics) and exact optimization (integer linear programming, decomposition methods) to overcome the complexity of the related problems (NP-hard problems).

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Loco2 activity

The LOgistique COopérative et Optimisation (LOCO2) team focuses on the modelling and optimization of production and transportation planning activities. The work is oriented towards the implementation of advanced methodologies in approximate optimization (metaheuristics) and exact optimization (integer linear programming, decomposition methods) to overcome the complexity of the related problems (NP-hard problems).

Two fields of application are privileged by integrating the dimension of emissions reduction in the objective function:

This axis focuses on multi-level vehicle routing problems, flow problems in distribution systems (in collaboration with Kyoto University and the Inria center of the University of Bordeaux).

In the framework of the factory of the future, this work targets on workshop layout problems equipped with transport systems based on autonomous mobile robots.

Loco_Presentation

LOCO2 skills

Modeling and Optimization

Discrete Event Simulation

Cooperative Game Theory

Supply Chain Planning (Timber industry)

Reverse logistics

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Members

Staff

Meet the members of the research team

Anne ZOUGGAR
Anne SEGUY
Frédéric PEREYROL
Sylvain LICHAU
Remy DUPAS
Pascale FARTHOUAT
Jean-Baptiste CLARION
Jean-Christophe DESCHAMPS
Résumé en français

L’équipe LOgistique COopérative et Optimisation (LOCO2) est centrée sur la modélisation et l’optimisation des activités de planification en production et transport. Les travaux sont orientés vers la mise en œuvre de méthodologies avancées en optimisation approchée (metaheuristiques) et exacte (programmation linéaire en nombres entiers, méthodes de décomposition) permettant de lever des verrous issus de la complexité des problèmes afférents (problèmes NP-difficiles). Deux domaines d’application sont privilégiés en intégrant la dimension de réduction des émissions dans la fonction d’objectif: la logistique urbaine et les systèmes de production reconfigurable

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