Jeffrey B. Schamburg

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The response surface methodology (RSM) provides an iterative process for learning that involves the sequential use of experimental design, empirical model building, and analysis of the developed models. This work provides a modified response surface methodology (MRSM) that can be applied to more complex simulation studies. These problems involve a larger(More)
In this paper we describe a methodology that includes the complementary use of simulated annealing and response surface methodology (RSM). The methodology was developed for analysis of simulations to help determine procedures for the employment of superheterodyne surveillance receivers. In this methodology, we use simulated annealing to determine near(More)
The United States Army's One Semi-Automated Forces (OneSAF) Objective System (OOS) is the next generation of Army high resolution combat models. Its development has leveraged the ever-increasing computing power available today to represent highly complex battlefield phenomena, particularly human behavior. In the fall of 2005, the Product Manager (PM) OneSAF(More)
This work provides a generalization of the traditional response surface methodology (RSM) that can be applied to complex, multi-objective simulation studies. These problems involve a larger number of input variables, multiple measures of performance, and complex systems relationships. This multiple RSM approach capitalizes on the underlying learning(More)
The Army acquisition community requires high-resolution simulations that represent the dismounted infantry soldier in enough detail to conduct an analysis of alternatives (AOA) for individual weapons and equipment. These models must also be capable of assessing future, proposed capabilities and technologies. Previous work established a detailed,(More)
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