Multivariate input modeling with Johnson distributions

Authors:
Paul M. Stanfield;James R. Wilson;Gary A. Mirka;Naomi F. Glasscock;Jennie P. Psihogios;Joseph R. Davis
Affiliations:
ABCO Automation, Inc., 6202 Technology Drive, Brown Summit, NC;Department of Industrial Engineering, North Carolina State University, Raleigh, NC;Ergonomics Laboratory, Department of Industrial Engineering, North Carolina State University, Raleigh, NC;Ergonomics Laboratory, Department of Industrial Engineering, North Carolina State University, Raleigh, NC;Ergonomics Laboratory, Department of Industrial Engineering, North Carolina State University, Raleigh, NC;Ergonomics Laboratory, Department of Industrial Engineering, North Carolina State University, Raleigh, NC
Venue:
WSC '96 Proceedings of the 28th conference on Winter simulation
Year:
1996

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Abstract

This paper introduces a new method for multivariate simulation input modeling based on the Johnson translation system of probability distributions. This technique matches the first four marginal moments and the correlation structure of a given set of sample data, allowing computationally efficient parameter estimation and random-vector generation. Applications of the technique in ergonomics and production scheduling are discussed. The proposed method is compared to traditional multivariate input-modeling techniques based on the Johnson translation system.