Objects. The ~ b s t rac t ~ b j ect forms the fundamental base class for the entire design and all other classes are derived from this base class. The Abstract Object class defines and characterizes all the essential properties every class in this 404 OBJECT-ORIENTED SIMULATION

Handbook of Simulation

This research investigates various strategies for assigning pseudorandom numbers to experimental points in statistically designed simulation and distribution sampling experiments. Strategies studied include the widely advocated practices of (i) employing a common set of pseudorandom numbers for all experimental points, and (ii) assigning a unique set of pseudorandom numbers to each experimental point. An alternative, based upon blocking concepts in designed experiments, is devised and shown to improve upon existing recommendations for a wide class of problems. A small simulation, a pilot study of a hospital resource allocation problem, illustrates the new strategy.

Pseudorandom Number Assignment in Statistically Designed Simulation and Distribution Sampling Experiments

The efficiency of several variance reduction techniques (in particular, importance sampling, stratified sampling, and control variates) are studied with respect to their application in estimating circuit yields. This study suggests that one essentially has to have a good approximation of the region of acceptability in order to achieve significant variance reduction. Further, all the methods considered are based, either explicitly or implicity, on the use of a model. The control variate method appears to be more practical for implementation in a general purpose statistical circuit analysis program. Stratified sampling is the most simple to implement, but yields only very modest reductions in the variance of the yield estimator. Lastly, importance sampling is very useful when there are few parameters and the yield is very high or very low; however, a good practical technique for its implementation, in general, has not been found.

A Study of Variance Reduction Techniques for Estimating Circuit Yields

An integrated intraseasonal and interseasonal stochastic dynamic programing model is developed to determine an optimal decision rule with respect to the following three classes of crop irrigation decisions: (1) intertemporal water application rates, (2) whether or not some acreage should be ‘abandoned’ from further irrigations for the remainder of the season, and (3) the optimal acreage to plant for potential irrigation at the beginning of the season. Solutions of the problem are shown to be a basis for optimizing the levels of three design variables, namely, developed irrigation acreage, reservoir capacity, and distribution system capacity. A method is presented for incorporating variance, as well as expected value, of net benefits into the decision criterion for optimal developed acreage. An application is made to a simplified real situation in which optimal acreage to develop is the only design variable. State variable transition probabilities are calculated by a simulation model. A significant trade off was found between expected net benefits and their variability in determination of the optimal developed acreage for irrigation.

Stochastic reservoir management and system design for irrigation

Confirmation of water quality models

The design of computer simulation experiments

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The design of computer simulation experiments