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Discrete-event simulation optimization is a problem of significant interest to practitioners interested in extracting useful information about an actual (or yet to be designed) system that can be modeled using discrete-event simulation. This paper presents a survey of the literature on discrete-event simulation optimization published in recent years (1988 to the present), with a particular focus on discrete input parameter optimization. The discrete input parameter case differentiates techniques appropriate for small and for large numbers of feasible input parameter values. Examples of applications that illustrate these methods are also discussed.

We develop and study general-purpose techniques for improving the e ciency of the stochastic mesh method that was recently developed for pricing American options via Monte Carlo simulation. First, we d e v elop a mesh-based, biased-low estimator. By recursively averaging the low and high estimators at each stage, we obtain a signi cantly more accurate point estimator at each of the mesh points. Second, we adapt the importance sampling ideas for simulation of European path-dependent options in Glasserman, Heidelberger, and Shahabuddin (1998a) to pricing of American options with a stochastic mesh. Third, we s k etch generalizations of the mesh method and we discuss links with other techniques for valuing American options. Our empirical results show that the bias-reduced point estimates are much more accurate than the standard mesh-method point estimates. Importance sampling is found to increase accuracy for a smooth optionpayo functions, while variance increases are possible for non-smooth payo s.

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This chapter reports on a mathematics professor's experience leveraging laptops in a required intermediate statistics course with a challenging student population.Use of laptops streamlined course delivery, enhanced classroom interaction, and improved both his students' and his own overall course experience.