Spreadsheet simulation

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Spreadsheet simulation

Full text

Pdf (91 KB)

Source

Winter Simulation Conference archive
Proceedings of the 33nd conference on Winter simulation table of contents

Arlington, Virginia

TUTORIAL SESSION: Introductory tutorials table of contents

Pages: 74 - 79

Year of Publication: 2001

ISBN:0-7803-7309-X

Author

Andrew F. Seila

The University of Georgia, Athens, GA

Sponsors

INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
SCS : The Society for Computer 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

IEEE Computer Society Washington, DC, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 13, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues peer to peer

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, discusses how to setup a spreadsheet simulation, and finally examines 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	F. Fumero , C. Vercellis , Hai Yang , Thomas A. Grossman, Teachers' Forum: Spreadsheet Modeling and Simulation Improves Understanding of Queues, Interfaces, v.29 n.3, p.88-103, March 1999 [doi>10.1287/inte.29.3.88]
	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	Cliff T. Ragsdale, Spreadsheet Modeling and Decision Analysis, South-Western Thomson Learning, 2000
	8	Wayne L. Winston, Simulation Modeling Using @Risk, Wadsworth Publ. Co., Belmont, CA, 1996