Changing the paradigm: Simulation, now a method of first resort

Decades ago, simulation was famously characterized as a “method of last resort,” to which analysts should turn only “when all else fails.” In those intervening decades, the technologies supporting simulation—computing hardware, simulation‐modeling paradigms, simulation software, design‐and‐analysis methods—have all advanced dramatically. We offer an updated view that simulation is now a very appealing option for modeling and analysis. When applied properly, simulation can provide fully as much insight, with as much precision as desired, as can exact analytical methods that are based on more restrictive assumptions. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Published 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 293–303, 2015

[1]  Gerd Folkers,et al.  On computable numbers , 2016 .

[2]  G. L. Buffon,et al.  Histoire naturelle générale et particuliere avec la description du Cabinet du Roy : tome premier , 2016 .

[3]  Frank Diederich,et al.  Simio And Simulation Modeling Analysis Applications , 2016 .

[4]  Susan M. Sanchez,et al.  Simulation experiments: Better data, not just big data , 2014, Proceedings of the Winter Simulation Conference 2014.

[5]  Alexander S. Szalay,et al.  The future of computerized decision making , 2014, Proceedings of the Winter Simulation Conference 2014.

[6]  W. David Kelton,et al.  Improving Voting Systems through Service‐Operations Management , 2014 .

[7]  Yoshihide Igarashi,et al.  Computing: A Historical and Technical Perspective , 2014 .

[8]  Jason R Andrews,et al.  Timing of tuberculosis transmission and the impact of household contact tracing. An agent-based simulation model. , 2014, American journal of respiratory and critical care medicine.

[9]  Karl Heinz Kienitz,et al.  Efficient, nearly orthogonal-and-balanced, mixed designs: an effective way to conduct trade-off analyses via simulation , 2013, J. Simulation.

[10]  W. Kelton,et al.  The call for equity: simulation optimization models to minimize the range of waiting times , 2013 .

[11]  W. Kelton,et al.  Simulation optimization for allocation of epidemic-control resources , 2013 .

[12]  S. Barry Cooper,et al.  On Computable Numbers, with an Application to the Entscheidungsproblem – A Correction , 2013 .

[13]  Hong Wan,et al.  Work smarter, not harder: A tutorial on designing and conducting simulation experiments , 2015, 2015 Winter Simulation Conference (WSC).

[14]  Thomas W. Lucas,et al.  The exponential expansion of simulation in research , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[15]  Jack J. Dongarra,et al.  Anatomy of a globally recursive embedded LINPACK benchmark , 2012, 2012 IEEE Conference on High Performance Extreme Computing.

[16]  Thomas W. Lucas,et al.  Designs for Large‐Scale Simulation Experiments, with Applications to Defense and Homeland Security , 2012 .

[17]  Lee W. Schruben,et al.  Simulation modeling for analysis , 2010, TOMC.

[18]  D. Sturdy.  Work smarter. , 2010, Nursing older people.

[19]  Sam Savage.  The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty , 2009 .

[20]  Mahmut Parlar,et al.  Dynamic Allocation of Airline Check-In Counters: A Queueing Optimization Approach , 2008, Manag. Sci..

[21]  Susan M. Sanchez,et al.  Work smarter, not harder: guidelines for designing simulation experiments , 2005, Proceedings of the Winter Simulation Conference, 2005..

[22]  Paul J. Sanchez,et al.  As Simple As Possible, but No Simpler: A Gentle Introduction to Simulation Modeling , 2006, Proceedings of the 2006 Winter Simulation Conference.

[23]  Jack P. C. Kleijnen,et al.  State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments , 2005, INFORMS J. Comput..

[24]  Thomas W. Lucas,et al.  When is model complexity too much? Illustrating the benefits of simple models with Hughes' salvo equations , 2003 .

[25]  Pierre L'Ecuyer,et al.  An Object-Oriented Random-Number Package with Many Long Streams and Substreams , 2002, Oper. Res..

[26]  Rhett Herman.  As simple as possible , 2002 .

[27]  Richard E. Nance,et al.  Perspectives on the Evolution of Simulation , 2002, Oper. Res..

[28]  R. Viertl.  On the Future of Data Analysis , 2002 .

[29]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[30]  D. J. Medeiros,et al.  Gpss Turns 40: Selected Perspectives , 2001 .

[31]  Thomas W. Lucas,et al.  The Stochastic Versus Deterministic Argument for Combat Simulations: Tales of When the Average Won't Do , 2000 .

[32]  W. Paul,et al.  Computer Architecture , 2000, Springer Berlin Heidelberg.

[33]  Jennifer S. Light,et al.  When Computers Were Women , 1999 .

[34]  Randall P. Sadowski,et al.  Simulation with Arena , 1998 .

[35]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[36]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[37]  John von Neumann,et al.  First draft of a report on the EDVAC , 1993, IEEE Annals of the History of Computing.

[38]  Anna J. Schwartz,et al.  Alternative Approaches to Analyzing Economic Data , 1991 .

[39]  I. Jolliffe,et al.  Simulation Methodology for Statisticians, Operations Analysts, and Engineers , 1990 .

[40]  W. Cleveland.  The Collected Works of John W. Tukey, Volume V, Graphics 1965-1985. , 1989 .

[41]  A. Alan B. Pritsker,et al.  Slam II network models for decision support , 1989 .

[42]  N. Balazs,et al.  Schr̈odinger: Life and Thought , 1989 .

[43]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[44]  F. S. Hillier,et al.  Introduction to Operations Research, 10th ed. , 1986 .

[45]  John W. Tukey,et al.  Philosophy and principles of data analysis , 1986 .

[46]  L. Schrage,et al.  A guide to simulation , 1983 .

[47]  Alice R. Burks,et al.  First General-Purpose Electronic Computer , 1981, Annals of the History of Computing.

[48]  G. Box.  Robustness in the Strategy of Scientific Model Building. , 1979 .

[49]  T. R. Featheringham,et al.  On systemic problem solving and the error of the third kind , 1974 .

[50]  H. M. Wagner,et al.  Principles of Operations Research: With Applications to Managerial Decisions. , 1971 .

[51]  David A. Patterson,et al.  Computer Architecture: A Quantitative Approach , 1969 .

[52]  G. Marsaglia.  Random numbers fall mainly in the planes. , 1968, Proceedings of the National Academy of Sciences of the United States of America.

[53]  R. Faure,et al.  Introduction to operations research , 1968 .

[54]  John Harling,et al.  Simulation Techniques in Operations Research---A Review , 1958 .

[55]  A. Turing.  On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[56]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[57]  이기수,et al.  II , 1856, My Karst and My City and Other Essays.