K. Preston White
- Courses5
- Reviews8
- School: University of Virginia
- Campus:
- Department: Engineering
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Location:
Charlottesville, VA - Dates at University of Virginia: December 2005 - November 2011
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Biography
University of Virginia - Engineering
Professor at University of Virginia
Higher Education
Preston
White
Charlottesville, Virginia Area
Prof. White's research interests include Monte Carlo and discrete-event simulation; scheduling and sequencing; statistical analysis, reliability, and probabilistic design; process and quality control; and logistics and supply chains. He is particularly interested in the integration of decision and information technologies and applications in aerospace, health-care, manufacturing, distribution, service, criminal justice, and military systems. He has published over 170 scholarly articles in these areas.
Experience
Carngie-Mellon University
Assistant Professor
Joint appointment in the Department of Mechanical Engineering and Department of Engineering and Public Policy
University of Virginia
Professor
K. Preston worked at University of Virginia as a Professor
Polytechnic Institute of New York
Assistant Professor
Department of Operations Research and Systems Analysis
Education
Duke Univeristy
Ph.D.
Systems Engineering
Duke University
BSE
Mechanical Engineering
Publications
Attribute Acceptance Sampling as a Tool for Verifying Requirements Using Monte Carlo Simulation
Quality Engineering/ American Society for Quality
Developed by quality engineers as an efficient statistical approach to verifying the acceptability of procured items within production environments, acceptance sampling can be generalized to other verification problems which similarly rely on the outcomes of stochastic sampling experiments. This article illustrates how the techniques and perspectives of attribute acceptance sampling can be adapted to the verification of probabilistic design requirements using Monte Carlo simulation. We consider requirements expressed as limit standards, specifying the performance indicator for conforming Monte Carlo trials; the minimum limiting proportion of conforming trials; and the maximum risk of accepting a nonconforming design. For such requirements, an attribute sampling plan prescribes the number of simulation replications that must be run and the number of nonconforming replications that can not be exceeded. The derivation and analysis of single-sample attribute acceptance plans for the mass-delivery requirement for NASA's Constellation Program (CxP) is provided as an example. More demanding (but potentially more efficient) alternatives to attribute acceptance sampling are suggested for applications with limited budgets for simulation trials.
Attribute Acceptance Sampling as a Tool for Verifying Requirements Using Monte Carlo Simulation
Quality Engineering/ American Society for Quality
Developed by quality engineers as an efficient statistical approach to verifying the acceptability of procured items within production environments, acceptance sampling can be generalized to other verification problems which similarly rely on the outcomes of stochastic sampling experiments. This article illustrates how the techniques and perspectives of attribute acceptance sampling can be adapted to the verification of probabilistic design requirements using Monte Carlo simulation. We consider requirements expressed as limit standards, specifying the performance indicator for conforming Monte Carlo trials; the minimum limiting proportion of conforming trials; and the maximum risk of accepting a nonconforming design. For such requirements, an attribute sampling plan prescribes the number of simulation replications that must be run and the number of nonconforming replications that can not be exceeded. The derivation and analysis of single-sample attribute acceptance plans for the mass-delivery requirement for NASA's Constellation Program (CxP) is provided as an example. More demanding (but potentially more efficient) alternatives to attribute acceptance sampling are suggested for applications with limited budgets for simulation trials.
A Framework for the Derivation and Verification of Variables Acceptance Sampling Plans
Investigacion Operacional
Acceptance sampling is a method for verifying quality or performance requirements using sample data. Variables acceptance sampling is an alternative to attributes acceptance sampling, which in many instances requires significantly smaller samples. In this paper, we consolidate the literature on variables acceptance sampling by providing a unified exposition of the approach used to develop such plans. From within this framework, we review the derivation of plans for exponential, normal, gamma, Weibull and Poisson random variables. We verified these derivations on a set of test problems.
