A time-based decomposition algorithm for fast simulation with mathematical programming models

Mathematical programming has been proposed in the literature as an alternative technique to simulate a special class of Discrete Event Systems. Several are the benefits of using a mathematical programming model for simulating but the non–linear computational time (in the number of simulated entities) needed for the solution of the models can be a huge barrier to its use in long simulations. This paper proposes a time–based decomposition algorithm that splits the mathematical programming model into a number of submodels to be solved sequentially so as to exploit the super–additivity of many non–linear functions and make the mathematical programming approach viable also for long run simulations. The number of needed submodels is the solution of an optimization problem that minimizes the expected time to solve all the submodels. The main result is that in this way the solution time becomes a linear function of the number of simulated entities.