The stochastic Buffer Allocation Problem (BAP) is well known in several fields and it has been characterized as NP-Hard. It deals with the optimal allocation of buffer spaces among stages of a system. Simulation Optimization is a possible way to approximately solve the problem. In particular, we refer to the Discrete Event Optimization (DEO). According to this approach, BAP simulation optimization can be modeled as a Mixed Integer Programming model. Despite the advantages deriving from having a single model for both simulation and optimization, its solution can be extremely demanding. In this work, we propose a Benders decomposition approach to efficiently solve large DEO of BAP, in which cuts are generated by simulation. Numerical experiment shows that the computation time can be significantly reduced by using this approach. Pedrielli, Matta, and Alfieri (2015) proposed a general DEO framework to model and optimize queueing systems. The approach relies on the Event Relationship Graph Lite (ERG Lite) formalism to formulate integrated simulation optimization mathematical programming models. ERG Lite is an extension of the Event Relationship Graphs. The authors showed that the BAP can be solved by DEO (Matta 2008) models that contain both simulation and optimization aspects. The simulation components control the event times, by means of constraints dealing with the system dynamics. The optimization components, instead, correspond to the binary variables and related constraints used for the capacity selection and minimization of total buffer space.