Most of the current Probabilistic Vehicle Routing (PVRP) Problem models simultaneously address only one stochastic aspect of the problem, and there is a need of more realistic PVRP models that can take into consideration more than one stochastic aspect in the same time. In this paper we propose a new stochastic PVRP algorithm that takes into consideration both uncertain transport demand and travel time. We propose a priori generalization strategy that can be either rigid or flexible in order to provide decision makers with rapid and adjustable solution schemes. A simulated annealing algorithm has been implemented to solve the PVRP with stochastic travel times in the context of chartered buses, and the results are quite satisfactory.

Most of the current Probabilistic Vehicle Routing (PVRP) Problem models simultaneously address only one stochastic aspect of the problem, and there is a need of more realistic PVRP models that can take into consideration more than one stochastic aspect in the same time. In this paper we propose a ...