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Feasibility study of variance reduction in the Logistics Composite model

The logistics composite model (LCOM) is a stochastic, discrete-event simulation that relies on probabilities and random number generators to model scenarios in a maintenance unit and estimate optimal manpower levels through an iterative process. Models such as LCOM involving pseudo-random numbers inevitably have a variance associated with the output of the model for each run. Reducing this output variance can be costly in the additional time needed for multiple replications. This research explores the application of three different methods for reducing the variance of the model's output. The methods include common random numbers, control variates, and antithetic variates. The result is a successful variance reduction in the primary output statistics of interest using the application of the control variates technique, as well as a methodology for the implementation of control variates in LCOM.