Optimal budget allocation for discrete-event simulation experiments

Simulation plays a vital role in analyzing discrete-event systems, particularly in comparing alternative system designs with a view to optimizing system performance. Using simulation to analyze complex systems, however, can be both prohibitively expensive and time-consuming. Effective algorithms to allocate intelligently a computing budget for discrete-event simulation experiments are presented in this paper. These algorithms dynamically determine the simulation lengths for all simulation experiments and thus significantly improve simulation efficiency under the constraint of a given computing budget. Numerical illustrations are provided and the algorithms are compared with traditional two-stage ranking-and-selection procedures through numerical experiments. Although the proposed approach is based on heuristics, the numerical results indicate that it is much more efficient than the compared procedures.