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In this study, stochastic vehicle routing problem (SVRP), which is one of the most important combinatorial optimization problems studied in the literature, is discussed. As it is known, the capacities of the vehicles and the demands of the customers are known in the classical vehicle routing problem, so the problem is deterministic. Since the problem parameters in real life problems vary according to the different situations, it is rare to know the exact values of the parameters. Therefore, there is a need to formulate the classical vehicle routing problem under uncertainty conditions. In this study, the vehicle routing problem is analyzed, and the demands are stochastically evaluated for the cases where customer demands are uncertain. In order to examine the variable demand conditions, the effects of these distributions on the solutions of the problem are investigated by using three different distributions, which are uniform, exponential, and Poisson. The GAMS software is used for computational results and the results of the stochastic and deterministic models of the problem are compared at the final part of the study. [ABSTRACT FROM AUTHOR]