Optimal computing budget allocation for multi-objective simulation optimization

Abstract

Complex systems are very common in real world situations and multiple performance measures are usually of interest. Simulation has been widely employed in evaluating these systems and selecting the desired ones. Performances of these systems are frequently stochastic in nature and therefore selection based on simulation output bears uncertainty. Correct selection would require considerable sampling from simulation models. However, simulation runs of complex systems tend to be expensive and simulation budget is often limited. It is therefore vital to determine an optimal sampling allocation strategy such that the desired systems can be correctly selected with the highest confidence.

This thesis describes how computing budget allocation concerns are addressed in the multi-objective simulation optimization context. The concept of Pareto optimality is incorporated to resolve the trade-offs between the multiple competing performance measures, where preference of a decision maker is not required. Evidence of correct selection is maximized through mathematical programming models that are built from either a probability or a large deviations perspective. Finite time performance and asymptotic properties of the proposed strategies are both investigated.