Rebooting Simulation

Abstract

AbstractComputer simulation has been in the toolkit of industrial engineers for over fifty years and its value has been enhanced by advances in research, including both modeling and analysis, and in application software, both commercial and open source. However, “advances” are different from paradigm shifts. Motivated by big data, big computing and the big consequences of model-based decisions, it is time to reboot simulation for industrial engineering.Keywords: Systems simulationbig datahigh-performance computingsystem of systemsDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsBarry L. NelsonBarry L. Nelson is the Walter P. Murphy Professor Emeritus of the Department of Industrial Engineering and Management Sciences at Northwestern. His research focus is the design and analysis of computer simulation experiments on models of discrete-event, stochastic systems, including methodology for simulation optimization, quantifying and reducing model risk, variance reduction, output analysis, metamodeling and multivariate input modeling. He has published numerous papers and three books, including Foundations and Methods of Stochastic Simulation: A First Course (second edition, Springer, 2021). Nelson is a Fellow of INFORMS and IISE. Further information can be found at www.iems.northwestern.edu/∼nelsonb/.Barry L. Nelson is the Walter P. Murphy Professor Emeritus of the Department of Industrial Engineering and Management Sciences at Northwestern. His research focus is the design and analysis of computer simulation experiments on models of discrete-event, stochastic systems, including methodology for simulation optimization, quantifying and reducing model risk, variance reduction, output analysis, metamodeling and multivariate input modeling. His application areas are manufacturing, services, financial engineering, renewable energy generation and transportation. He has published numerous papers and three books, including Foundations and Methods of Stochastic Simulation: A First Course (second edition Springer, 2021). Nelson is a Fellow of INFORMS and IISE. In 2006, 2013 and 2015 he received the Outstanding Simulation Publication Award from the INFORMS Simulation Society; in 2009, 2011 and 2015 he was awarded the Best Paper–Operations Award from IIE Transactions; in 2019 he received the David F. Baker Distinguished Research Award from IISE; and in 2022 he received the Lifetime Professional Achievement Award from the INFORMS Simulation Society. His teaching has been acknowledged by a Northwestern University Alumni Association Excellence in Teaching Award, a McCormick School of Engineering & Applied Science Teacher of the Year Award (twice), and the IISE Operations Research Division, and IISE Simulation and Modeling Division, Awards for Excellence in Teaching. Further information can be found at www.iems.northwestern.edu/∼nelsonb/.