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

Linda Trocine, Nicholas Cummings, Ashley Bazzana, Nathan Rychlik, Kenneth LeCroy, Grant Cates, Mansooreh Mollaghasemi
2010 SpaceOps 2010 Conference   unpublished
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
more » ... very 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 lessdetailed models. The DES team continues to innovate and expand DES capabilities to address KSC's planning needs. Nomenclature a = alpha, Type I error, Producer's risk R = beta, Type II error, Consumer's risk Ho
doi:10.2514/6.2010-2027 fatcat:ighvnja4czhorctcplbvuzuj74