Joint Resource Allocation for Input Data Collection and Simulation

Abstract

Simulation is often used to evaluate and compare performances of stochastic systems, where the underlying stochastic models are estimated from real-world input data. Collecting more input data can derive closer-to-reality stochastic models while generating more simulation replications can reduce stochastic errors. With the objective of selecting the system with the best performance, we propose a general framework to analyze the joint resource allocation problem for collecting input data and generating simulation replications. Two commonly arised features, correlation in input data and common random numbers in simulation, are jointly exploited to save cost and enhance efficiency. In presence of both features, closed-form joint resource allocation solutions are given for the comparison of two systems.