ACM Home Page
Please provide us with feedback. Feedback
Spreadsheet simulation: spreadsheet simulation
Full text PdfPdf (124 KB)
Source Winter Simulation Conference archive
Proceedings of the 35th conference on Winter simulation: driving innovation table of contents
New Orleans, Louisiana
SESSION: Introductory tutorials table of contents
Pages: 25 - 30  
Year of Publication: 2003
ISBN:0-7803-8132-7
Author
Andrew F. Seila  The University of Georgia, Athens, GA
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
Winter Simulation Conference 
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 29,   Citation Count: 0
Additional Information:

abstract   references   collaborative colleagues  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   

ABSTRACT

"Spreadsheet simulation" refers to the use of a spreadsheet as a platform for representing simulation models and performing the simulation experiment. This tutorial explains the reasons for using this platform for simulation, discusses why this is frequently an efficient way to build simulation models and execute them, describes how to setup a spreadsheet simulation, and finally suggests when a spreadsheet is not an appropriate platform for simulation.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Alexopoulos, C., and A. F. Seila. 1998. Output data analysis. In Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. J. Banks. New York: John Wiley.
 
2
Cheng, R. C. H. 1998. Random variate generation. In Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. J. Banks. New York: John Wiley.
 
3
Fishman, G. S. 1996. Monte Carlo concepts, algorithms and applications. New York: Springer.
 
4
 
5
L'Ecuyer, P. 1998. Random number generation. In Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. J. Banks. New York: John Wiley.
 
6
Mattesich, R. 1961. Budgeting models and system simulation. The Accounting Review 36:384--397.
 
7
Seila, A. F., V. Ceric, and P. Tadikamalla. 2003. Applied simulation modeling. Belmont, California: Brooks-Cole.
 
8