Laboratoire de Génie Informatique et d’Automatique de l’Artois

Nathalie HELAL

Ph.D. student
Member of the research axes:
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Le problème de tournées de véhicules avec des demandes évidentielles
Actes des 26e rencontres Francophones sur la Logique Floue et ses Applications , pp 15-21, (Prix ex aequo du meilleur papier doctorant), Amiens, France, Cepaduès, octobre 2017
A Recourse Approach for the Capacitated Vehicle Routing Problem with Evidential Demands
Proceedings of the 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017, pp 190-200, LNCS 10369, Lugano, Switzerland, A. Antonucci, L. Cholvy and O. Papini (Eds.), Springer, juillet 2017
The Capacitated Vehicle Routing Problem with Evidential Demands: A Belief-Constrained Programming Approach
4th International Conference on Belief Functions, BELIEF 2016, pp 212-221, LNAI 9861, Prague, Czech Republic, J. Vejnarová and V. Kratochvil (Eds.), Springer, septembre 2016
2016
French conference with review committee
Optimisation discrète sous incertitudes modélisées par des fonctions de croyance
17ème congrès ROADEF de la société Française de Recherche Opérationnelle et Aide à la Décision, Compiègne, France, février 2016

Ph.D. topic: "Optimisation under uncertainty modeled by belief functions: application to logistics and transport"

2014

The last decades have seen a surge of interest in optimization problems that take into consideration different forms of uncertainties. Indeed, many real-life problems require finding a (sub-) optimal solution without knowing in advance very precise values for all input data. For instance, uncertainties can arise from unpredictable environmental conditions that can influence the demand, the cost, or the availability of certain products. In other applications, the uncertainties can concern the decision variables: it can even be difficult to determine in advance the feasibility/existence of certain candidate solutions.

Successful existing approaches to deal with uncertainty include stochastic programming or robust optimization methods. This PhD subject is devoted to a new framework of addressing uncertainty: the theory of belief functions. This is a very convenient framework to represent and combine uncertain information (evidence) in a relatively natural manner. While more classical stochastic optimization methods consider that uncertainty can be be described using exact probabilistic laws, the belief functions are more useful to formalize non-random uncertainty with no precise distribution laws. As such, we will use this framework to address belief-based uncertainty in optimization. Over a longer term, the goal is to develop a versatile and flexible framework of addressing this type of uncertainty in optimization. These methods will be applied to the vehicle routing problem.