A soft systems approach to input distribution estimation for a non-stationary demand process
Pages 982 - 987
Abstract
A soft systems methodology is undertaken to develop a design for a communications system to support a political campaign. The focus is on the methodology to estimate an input distribution for a SLAM-based simulation model. Two problems-the lack of referent system data and the expectation of non-stationary system demand-indicate a need for a soft systems approach. The results of this study suggest that the approach is effective for estimating an input distribution for simulation analysis. The case provides an example of the contrast between the soft systems and hard systems approaches.
References
[1]
Burgess, G. M. et al. 1992. The Application of Causal Maps to Develop a Collective Understanding of Complex Organizational Contexts in Requirements Analysis. Accounting, Management and lnfoJTnation Technologies 2,3: 143-164.
[2]
Byrd, T. A. et al. 1992. A Synthesis of Research on Requirements Analysis and Knowledge Acquisition Techniques. MIS Quarterly 16"117-138.
[3]
Cavaleri, S., and K. Obloj. 1993. Management Systems: A Global Perspective. Belmont, California: Wadsworth, Inc.
[4]
Chcckland, P. 1981. Systems Thinking, Systems Practice. New York: John Wiley and Sons.
[5]
Forrester, J. W. 1961. hTdustrial Dynamics. Cambridge, Massachusetts: The MIT Press.
[6]
Lane, D. C. Modeling as Learning: A Consultancy Methodology for Enhancing Learning in Management Teams. 1994. in Morecroft and Sterman, Modeling for Learning Organizations. Portland, Oregon: Productivity Press.
[7]
Law, A. M., and W. D. Kelton. 1991. Simulation Modeling and Analysis. New York: McGraw-Hill, Inc.
[8]
Pritsker, A. A. B. 1986. Introduction to Simulation and SLAM 11. 3rd ed. West Lafayette, indiana: Systems Publishing Corporation.
[9]
Senge, P. M. 1991. The Fifth Discipline. New York: Doubleday/Currency.
[10]
Shanker, A. and W. D. Kelton. 1991. Empirical Input Distributions: An Alternative to Standard Input Distributions in Simulation Modeling. In Proceedings of the 1991 Winter Simulation Conference, 978-985. Institute of Electrical and Electronics Engineers, San Francisco, California.
[11]
Vennix, A. M. and J. W. Gubbels. 1994. Knowledge Elicitation in Conceptual Model Building: A Case Study in Modeling a Regional Dutch Health Care System. in Morecroft and Sterman, Modeling for Learning Organizations, Portland: Productivity Press.
[12]
Vincent, S. G. and W. D. Kelton. 1992. Distribution Selection and Validation. In Proceedings of the 1991 Winter Simulation Conference, 300-304. Institute of Electrical and Electronics Engineers, San Francisco, California.
Index Terms
- A soft systems approach to input distribution estimation for a non-stationary demand process
Recommendations
Soft topological soft groups and soft rings
In this paper, we produce and examine several new notions such as soft topological soft groups and their subsystem. Also, soft topological soft rings and their subsystems are introduced. Furthermore, we introduce some new concepts like softly ...
Soft ωs-irresoluteness and soft pre-ωs-openness insoft topological spaces
We use soft ωs-open sets to define soft ωs-irresoluteness, soft ωs-openness, and soft pre-ωs-openness as three new classes of soft mappings. We give several characterizations for each of them, specially via soft ωs-closure and soft ωs-interior soft ...
Soft groups and normalistic soft groups
Soft set theory, proposed by Molodtsov, has been regarded as an effective mathematical tool to deal with uncertainties. In this paper, first we correct some of the problematic cases in a previous paper by Aktas and Cag@?man [H. Aktas, N. Cag@?man, Soft ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 1995
1493 pages
ISBN:0780330188
- Chairmen:
- William R. Lilegdon,
- David Goldsman,
- Editors:
- Christos Alexopoulos,
- Keebom Kang
Sponsors
- IIE: Institute of Industrial Engineers
- IEEE-SMCS: Systems, Man & Cybernetics Society
- INFORMS/CS: Computer Science TC
- ASA: American Statistical Association
- ACM: Association for Computing Machinery
- SCS: Society for Computer Simulation
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- IEEE-CS: Computer Society
- NIST: National Institute of Standards & Technology
Publisher
IEEE Computer Society
United States
Publication History
Published: 01 December 1995
Check for updates
Qualifiers
- Article
Conference
Acceptance Rates
WSC '95 Paper Acceptance Rate 122 of 183 submissions, 67%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 428Total Downloads
- Downloads (Last 12 months)56
- Downloads (Last 6 weeks)11
Reflects downloads up to 17 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in