Relative effectiveness of mechanisms for simulating uncertainty in quantitative systems

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 217, Issue 4, 2003, Pages 531-540

  Pons, D J ^a   Raine, J K ^b  

^a Christchurch Heart Institute   (New Zealand)

^b UNIVERSITY OF CANTERBURY   (New Zealand)

Author keywords

Design; Fuzzy theory uncertainty; Monte Carlo; Probabilistic computation; Quantitative random variables

Indexed keywords

ALGEBRA; COMPUTATIONAL METHODS; FUZZY SETS; MACHINE DESIGN; MONTE CARLO METHODS; OPTIMIZATION; PROBABILITY; PROCESS ENGINEERING; PRODUCT DESIGN; UNCERTAIN SYSTEMS;

ENGINEERING DESIGN PROCESS; ENGINEERING SYSTEMS; QUANTITATIVE RANDOM VARIABLES; QUANTITATIVE SYSTEMS; SYSTEM INTEGRITY;

COMPUTER SIMULATION;

EID: 0038398777     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/095440503321628198     Document Type: Article

References (19)

1. Raine J., Pons, D. and Whybrew, K. The design process and a methodology for system integrity. Proc. Instn Mech. Engrs, Part B: J. Engineering Manufacture, 2001, 215(B4), 569-576.
2. Pons, D. A methodology for system integrity in design. PhD thesis, University of Canterbury, Christchurch, New Zealand, 2001.
3. Ackoff, R. L. Scientific Method Optimising Applied Research Decisions, 1962 (John Wiley, New York).
4. Springer, M. D. The Algebra of Random Variables, 1979 (John Wiley, New York).
5. Syski, R. Random Processes, 2nd edition, 1989 (Marcel Dekker, New York).
6. Kleijnen, J. P. C. Verification and validation of simulation models. Eur. J. Opl Res., 1995, 82(1), 145-162.
7. Zhang, S., Mcallister, J. and Dorricott, M. G. Probabilistic analysis of risk in mining projects - as easy as 123. In 3rd Large Open Pit Mining Conference, 30 August-3 September 1992, pp. 455-459.
8. Duckworth, B., Clarson, D. S., Vreeburg, J., Rosenthal, L., Poortema, K. and Hoogsteen, K. Risk management. Water Supply, 1998, 16(1-2), 467-471.
9. Basu, A. Practical risk analysis in scheduling. In AACE International, Transactions of the Annual Meeting, 28 June-1 July 1998, 4pp.
10. Kostetsky, O. Facilitated, graphics and Monte Carlo based predictive project process. In IEEE International Engineering Management Conference, 17-19 October 1994, pp. 177-184.
11. Ross, S. M. Simulation, 2nd edition, 1997 (Academic Press, San Diego, California).
12. @ Risk, 2001: http://www.palisade.com/.
13. Analytica, 2001; http://www.lumina.com/.
14. Wood, K. L., Antonsson, E. K. and Beck, J. L. Comparing fuzzy and probability calculus for representing imprecision in preliminary engineering design. In Design Theory and Methodology (Ed. W. P. Seering), 1989, pp. 99-105 (American Society of Mechanical Engineers, New York). Presented at the First International ASME Design Theory and Methodology Conference (DTM '89), Montreal Canada, 18-19 September 1989.
15. Wood, K. L. and Antonsson, E. K. Computations with imprecise parameters in engineering design: background and theory. J. Mechanisms, Transm., and Automn in Des., 1989, 111(4), 616-625.
16. Cooper, D. F. and Chapman, C. B. Risk Analysis for Large Projects, 1987 (John Wiley, Chichester and New York).
17. Powell, P. B. Software Validation, Verification and Testing Technique and Tool Reference Guide, NBS Special Publication 500-93, 1982.
18. Vose, D. Quantitative Risk Analysis, 1996 (John Wiley, Chichester and New York).
19. Quelch, J. and Cameron, I. T. Uncertainty representation and propagation in quantified risk assessment using fuzzy sets. J. Loss Prev. in Process Industries, 1994, 7(6), 463-473.