IMPORTANCE SAMPLING SIMULATION IN ULTRASAN

Traditional simulation techniques perform poorly when estimating performance measures based on rare events. One solution to this problem is the use of importance sampling. However, two problems that have limited the use of importance sampling are the lack of a formal framework for specifying importance sampling strategies, and the fact that in most cases the simulations must be hand-coded - a very time-consuming process. This paper presents a software tool that facilitates experimentation with importance sampling by addressing the two problems. First, the tool is based on a flexible framework for specifying importance sampling simulations in terms of stochastic activity networks. Second, once specified, the importance sampling simulation program is automatically generated by the tool, freeing the researcher to focus on the modeling problem. The effectiveness of the software is demonstrated through the solution of a machine-repairman model with Weibull distributed failure times and a delayed group repair policy. Orders of magnitude reduction in the CPU time required to obtain a specified relative accuracy were achieved.