The Capacitated Vehicle Routing Problem with Evidential Demands: a Belief-Constrained Programming Approach

Nathalie Helal (1) , Frédéric Pichon (1) , Daniel Cosmin Porumbel (2) , David Mercier (1) , Éric Lefèvre (1)

1 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois
2 CEDRIC - OC - CEDRIC. Optimisation Combinatoire

This paper studies a vehicle routing problem, where vehicles have a limited capacity and customer demands are uncertain and represented by belief functions. More specifically, this problem is formalized using a belief function based extension of the chance-constrained programming approach, which is a classical modeling of stochastic mathematical programs. In addition, it is shown how the optimal solution cost is influenced by some important parameters involved in the model. Finally, some instances of this difficult problem are solved using a simulated annealing metaheuristic, demonstrating the feasibility of the approach.

Mots clés: Vehicle routing problem; Stochastic programming; Chance-constrained programming; Belief functions

Domaines: Recherche opérationnelle [math.OC]; Intelligence artificielle [cs.AI]

Nathalie Helal, Frédéric Pichon, Daniel Cosmin Porumbel, David Mercier, Éric Lefèvre. The Capacitated Vehicle Routing Problem with Evidential Demands: a Belief-Constrained Programming Approach. BELIEF'2016, Sep 2016, Prague, Czech Republic. pp.212-221, ⟨10.1007/978-3-319-45559-4_22⟩. ⟨hal-03337226⟩