9409266 Jacobson The objective of this research is to introduce a new set of tools for building and analyzing simulation models of complex systems. Issues considered include model verification and validation, stopping conditions, variance reduction techniques, sensitivity analysis, and experimental design. The theory of computational complexity is used to provide a unified framework to classify and study the issues. A new heuristic algorithm will also be developed to address these issues. Six decision problems fundamental to simulation modeling and analysis are identified and may be proven to be non-deterministic polynomial complete. Special cases of the decision problems that are solvable in polynomial time are studied. New heuristics for these problems are developed based on tree search, dynamic programming, and simulated annealing. The heuristics developed in this research have the potential to provide quantitative, automated approaches to address fundamental decision elements in discrete event simulation. The developed tools when attached to simulation languages can help practitioners build more precise, better quality models, and perform analysis with a higher degree of assurance and confidence. The outcome of this research has the potential to advance the domain of systems that can be modeled and analyzed using discrete event simulation.
