Contrasts two efficient methods of sensitivity analysis, score function and pertubation analysis, and discusses conventional statistical experimental design. Some background is also given to simulation and modelling, and the authors include case studies using the score function method.
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Uses a conceptual approach to deal with sensitivities of the performance measure with respect to decision variables and offers guidance in improving decisions and identifying the most important ones. Topics covered include sensitivity analysis and optimization of discrete event static and discrete event dynamic systems, a unified framework for the SF method, important sampling, rare events, bottleneck networks and extensions such as autocorrelated input processes.
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