Input modeling when simple models fail

Authors:
Barry L. Nelson;Marne C. Cario;Chester A. Harris;Stephanie A. Jamison;J. O. Miller;James Steinbugl;Jaehwan Yang;Peter Ware
Affiliations:
Dept. of Industrial Engr & Management Sciences, Northwestern University, Evanston, Illinois;Dept. of Industrial, Welding & Systems Engr, The Ohio State University, Columbus, Ohio;Dept. of Industrial, Welding & Systems Engr, The Ohio State University, Columbus, Ohio;Dept. of Industrial, Welding & Systems Engr, The Ohio State University, Columbus, Ohio;Dept. of Industrial, Welding & Systems Engr, The Ohio State University, Columbus, Ohio;Dept. of Industrial, Welding & Systems Engr, The Ohio State University, Columbus, Ohio;Dept. of Industrial, Welding & Systems Engr, The Ohio State University, Columbus, Ohio;Dept. of Computer & Information Science, The Ohio State University, Columbus, Ohio
Venue:
WSC '95 Proceedings of the 27th conference on Winter simulation
Year:
1995

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Abstract

A simulation model is composed of inputs and logic; the inputs represent the uncertainty or randomness in the system, while the logic determines how the system reacts to the uncertain elements. Simple input models, consisting of independent and identically distributed sequences of random variates from standard probability distributions, are included in every commercial simulation language. Software to fit these distributions to data is also available. In this tutorial we describe input models that are useful when simple models are not.