index - Equipe : Aide à la Décision Access content directly

Industrial Engineering Departement

Decision Aid

The Decision Aid for Goods and Services' Systems Team (DA) is composed of 22 members among which 8 faculty members, 2 post-docs and 12 PhD students. The permanent members of the team come under the two ``CNU sections'': Computer science (27) and Computer engineering, automation and signal processing (61).

Research conducted refers to the scientific fields of Operational Research and Decision Analysis. The focus is related to decision support in operations management, and concerns both systems of goods and systems of services. Scientific issues are positioned at two levels. (i) At an application domain level, research issues are related to performance evaluation and optimization of systems of production and distribution of goods and services. (ii) Conceptual, methodological, procedural and algorithmic issues are tackled to meet application challenges.

The development of methods to optimize various aspects of organizational systems, leading to mobilize, adapt, reformulate or even develop formal tools so to effectively answer to the questions to which decision makers are confronted. These works often lead to developing new concepts, tools and decision support methodologies decision whose validity extends way beyond the application for which they were initially designed.

Our research is structured into three main research projects:

  • Service Operations Management (Healthcare systems, Call centers, ...)
  • Supply Chain Management (supply chain design and planning, cooperation and competition, flow and inventory management, green supply chain)
  • Multiple Criteria Decision Aid (preference modeling and elicitation, multiobjective optimization)

The models and resolutions methods considered in our research refers to Decision Aid/Operational Research: discrete event simulation methods, combinatorial optimization and mathematical programming, game theory, value based and outranking based preference models, argumentation models, preference learning, stochastic models, dynamic programming,


Nombre de documents


Nombre de notices


Répartition des dépôts par type de documents

Derniers dépôts

Chargement de la page