Estimation methods for passage times using one-dependent cycles

Abstract

The lengths of certain passage-time intervals (random time intervals) in discrete-event stochastic systems correspond to delays in computer, communication, manufacturing, and transportation systems. Simulation is often the only available means for analyzing a sequence of such lengths. It is sometimes possible to obtain meaningful estimates for the limiting average delay indirectly, that is, without measuring lengths of individual passage-time intervals. For general time-average limits of a sequence of delays, however, it is necessary to measure individual lengths and combine them to form point and interval estimates. We consider sequences of delays determined by state transitions of a generalized semi-Markov process and introduce a recursively-generated sequence of realvalued random vectors, called start vectors, to provide the link between the starts and terminations of passage-time intervals. This method of start vectors for measuring delays avoids the need to “tag” entities in the system. We show that if the generalized semi-Markov process has a recurrent single-state, then the sample paths of any sequence of delays can be decomposed into one-dependent, identically distributed cycles. We then show that an extension of the regenerative method for analysis of simulation output can be used to obtain meaningful point estimates and confidence intervals for time-average limits. This estimation procedure is valid not only when there are no ongoing passage times at any regeneration point but, unlike previous methods, also when the sequence of delays does not inherit regenerative structure. Application of these methods to a manufacturing cell with robots is discussed.