Some modest proposals for simulation software: design and analysis of experiments

Simulation software has made great advances in recent years along the dimensions of modeling capabilities, animated graphics, and ease of use. There have also been real improvements in terms of both generality to model a wide variety of systems, and in specialization for quick and accurate modeling in specific application domains. And, of course, the dramatic improvement in computing/cost ratios has rendered truly commonplace what were just a few years ago impossibly time- and memory-consuming simulations. However, the statistical design-and-analysis capabilities of simulation software have not kept pace with the modeling, graphics, and ease-of-use advances; nor have they kept pace with ongoing research on the underlying methods. This paper discusses a wide variety of design-and-analysis capabilities that the author feels should be as available and as easy-to-use as are current modeling and graphics functions. The list includes only methods that are in existence today, so is not just a "wish list" of perhaps impossible dreams. The paper concludes with speculation on why this imbalance exists and suggests how it might be effectively addressed.

[1]  Lee W. Schruben,et al.  A survey of simulation optimization techniques and procedures , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[2]  Susan M. Sanchez,et al.  Robust design: seeking the best of all possible worlds , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

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

[4]  Barry L. Nelson,et al.  A ranking and selection project: experiences from a university-industry collaboration , 1999, WSC '99.

[5]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[6]  Barry L. Nelson,et al.  Statistical screening, selection, and multiple comparison procedures in computer simulation , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[7]  P. L'Ecuyer,et al.  Uniform random number generators , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[8]  James R. Wilson.  Modeling dependencies in stochastic simulation inputs , 1997, WSC '97.

[9]  D. Goldsman,et al.  Output analysis procedures for computer simulations , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[10]  J. S. Dagpunar,et al.  Principles of Discrete Event Simulation , 1980 .

[11]  Lawrence Leemis,et al.  Input modeling , 1994, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[12]  Bruce W. Schmeiser.  Advanced input modeling for simulation experimentation , 1999, WSC '99.

[13]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[14]  Russell R. Barton,et al.  Experimental design for simulation , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[15]  Paul Bratley,et al.  A guide to simulation , 1983 .

[16]  W. David Kelton.  Perspectives on Simulation Research and Practice , 1994, INFORMS J. Comput..

[17]  S.M. Sanchez.  ABC's of output analysis , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[18]  Andrew F. Seila,et al.  Output analysis for simulation , 1991, 1991 Winter Simulation Conference Proceedings..

[19]  Alexander Shapiro.  Simulation based optimization , 1996, Winter Simulation Conference.

[20]  Pierre L'Ecuyer.  Uniform random number generators , 1998, WSC '98.

[21]  Barry L. Nelson,et al.  Input modeling tools for complex problems , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).