Research and Advances
Artificial Intelligence and Machine Learning

Analysis of future event set algorithms for discrete event simulation

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New analytical and empirical results for the performance of future event set algorithms in discrete event simulation are presented. These results provide a clear insight to the factors affecting algorithm performance, permit evaluation of the hold model, and determine the best algorithm(s) to use. The analytical results include a classification of distributions for efficient insertion scanning of a linear structure. In addition, it is shown that when more than one distribution is present, there is generally an increase in the probability that new insertions will have smaller times than those in the future event set. Twelve algorithms, including most of those recently proposed, were empirically evaluated using primarily simulation models. Of the twelve tested, four performed well, three performed fairly, and five performed poorly.

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