Statistical and Probabilistic Extensions to Ground Operations' Discrete Event Simulation Modeling

NASA's human exploration initiatives will invest in technologies, public/private partnerships, and infrastructure, paving the way for the expansion of human civilization into the solar system and beyond. As it is has been for the past half century, the Kennedy Space Center will be the embarkation point for humankind's journey into the cosmos. Functioning as a next generation space launch complex, Kennedy's launch pads, integration facilities, processing areas, launch and recovery ranges will bustle with the activities of the world's space transportation providers. In developing this complex, KSC teams work through the potential operational scenarios: conducting trade studies, planning and budgeting for expensive and limited resources, and simulating alternative operational schemes. Numerous tools, among them discrete event simulation (DES), were matured during the Constellation Program to conduct such analyses with the purpose of optimizing the launch complex for maximum efficiency, safety, and flexibility while minimizing life cycle costs. Discrete event simulation is a computer-based modeling technique for complex and dynamic systems where the state of the system changes at discrete points in time and whose inputs may include random variables. DES is used to assess timelines and throughput, and to support operability studies and contingency analyses. It is applicable to any space launch campaign and informs decision-makers of the effects of varying numbers of expensive resources and the impact of off nominal scenarios on measures of performance. In order to develop representative DES models, methods were adopted, exploited, or created to extend traditional uses of DES. The Delphi method was adopted and utilized for task duration estimation. DES software was exploited for probabilistic event variation. A roll-up process was used, which was developed to reuse models and model elements in other less - detailed models. The DES team continues to innovate and expand DES capabilities to address KSC's planning needs

Finding Important Independent Variables Through Screening Designs: A Comparison Of Methods

Once a simulation model is developed, designed experiments may be employed to efficiently optimize the system. Designed experiments are used on `real\u27 production systems as well. The first step is to screen for important independent variables. Several screening methods are compared and contrasted in terms of efficiency, effectiveness, and robustness. These screening methods range from the classical factorial designs and two-stage group screening to new, more novel designs including sequential bifurcation (SB) and iterated fractional factorial designs (IFFD). Conditions for the use of the methods are provided along with references on how to use them

An Overview Of Newer, Advanced Screening Methods For The Initial Phase In An Experimental Design

Screening is the first phase of an experimental study on systems and simulation models. Its purpose is to eliminate negligible factors so that efforts may be concentrated upon just the important ones. Successfully screening more than about 20 or 30 factors has been investigated only in the past 10 or 15 years with most improvements in the past 5 years. A handful of alternative methods including sequential bifurcation, iterated fractional factorial designs, and the Trocine Screening Procedure are described and evaluative and comparative results are presented

Data Mining And Traditional Regression

Data mining has emerged in response to a need from industry for effective and efficient analysis of large data sets. It is used with great success in the business world in areas such as marketing. Traditional regression techniques have not generally been used in data mining but may be more attractive in some applications. An example of such an application is military manpower analysis. We introduce how we will approach this problem using logistic and linear regression