Adaptive Control of Large-Scale Simulations

Abstract

This thesis develops adaptive simulation control techniques that differentiate between competing system configurations. Here, a system is a real world environment under analysis. In this context, proposed modifications to a system denoted by different configurations are evaluated using large-scale hybrid simulation. Adaptive control techniques, using ranking and selection methods, compare the relative worth of competing configurations and use these comparisons to control the number of required simulation observations. Adaptive techniques necessitate embedded statistical computations suitable for the variety of data found in detailed simulations, including hybrid and agent-based simulations. These embedded statistical computations apply efficient sampling methods to collect data from simulations running on a network of workstations. The National Airspace System provides a test case for the application of these techniques to the analysis and design of complex systems, implemented here in the Reconfigurable Flight Simulator, a large-scale hybrid simulation. Implications of these techniques for the use of simulation as a design activity are also presented.