Importance Sampling Simulation of San-based Reward Models

Importance sampling is a potentially powerful technique for e ciently estimating the probabilities of rare events in discrete event systems. The work presented here makes three contributions to importance sampling research. First, a convenient mechanism for specifying complex importance sampling heuristics is presented. Second, a method for applying importance sampling to stochastic activity networks at the network level is developed. Third, the speci cation mechanism and execution method are implemented as an integrated tool that can automatically generate an importance sampling simulation program from the speci cation of a heuristic and a stochastic activity network. The e ectiveness of the new software tool is illustrated through its application to a machine-repairman model with delayed group repair. Orders of magnitude reduction in the CPU time required to obtain a speci ed relative accuracy were achieved.