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.

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