Simulation of large networks: propagation of uncertainty in a simulation-based maritime risk assessment model utilizing Bayesian simulation techniques

ABSTRACT

Recent studies in the assessment of risk in maritime transportation systems have used simulation-based probabilistic techniques. Amongst them are the San Francisco Bay (SFB) Ferry exposure assessment in 2002, the Washington State Ferry (WFS) Risk Assessment in 1998 and the Prince William Sound (PWS) Risk Assessment in 1996. Representing uncertainty in such simulation models is fundamental to quantifying system risk. This paper illustrates the representation of uncertainty in simulation using Bayesian techniques to model input and output uncertainty. These uncertainty representations describe system randomness as well as lack of knowledge about the system. The study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the Bayesian simulation technique. Such characterization of uncertainty in simulation-based analysis provides the user with a greater level of information enabling improved decision making.

REFERENCES

• 1: Apostolakis, G. 1978. Probability and risk assessment: the subjectivist viewpoint and some suggestions. Nuclear Safety 19: 305--315.
• 2: Bedford, T. M., R. M. Cooke. 2001. Probabilistic Risk Analysis: Foundations and Method. Cambridge, UK: Cambridge University Press.
• 3: Chen, H. 1996. A lower bound for the correct subset selection probability and its application in discrete event simulations. IEEE Transactions on Automatic Control 41(8): 1227--1231.
• 4: Chen, H., B. Schmeiser. 1995. Monte Carlo estimation for guaranteed coverage non-normal tolerance intervals. Journal of Statistical Computation and Simulation 51(2--4): 223--238.
• 5: Hsiao-Chang Chen , Chun-Hung Chen , Jianwu Lin , Enver Yücesan, An asymptotic allocation for simultaneous simulation experiments, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.359-366, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324242]
• 6: Russell C. H. Cheng, Regression metamodeling in simulation using Bayesian methods, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.330-335, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324236]
• 7: Stephen E. Chick, Bayesian analysis for simulation input and output, Proceedings of the 29th conference on Winter simulation, p.253-260, December 07-10, 1997, Atlanta, Georgia, United States [doi>10.1145/268437.268488]
• 8: Stephen E. Chick, Bayesian methods: bayesian methods for simulation, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
• 9: Stephen E. Chick, Input Distribution Selection for Simulation Experiments: Accounting for Input Uncertainty, Operations Research, v.49 n.5, p.744-758, September 2001 [doi>10.1287/opre.49.5.744.10606]
• 10: Stephen E. Chick , Koichiro Inoue, New Two-Stage and Sequential Procedures for Selecting the Best Simulated System, Operations Research, v.49 n.5, p.732-743, September 2001 [doi>10.1287/opre.49.5.732.10615]
• 11: Cooke, R. M. 1991. Experts in Uncertainty: Expert Opinion and Subjective Probability in Science. Oxford, UK: Oxford University Press.
• 12: Fowler, T. G., E. Sorgard. 2000. Modeling ship transportation risk. Risk Analysis 20(2): 225--244.
• 13: Gelfand, A. 1996. Model determination using sampling-based methods. Markov Chain Monte Carlo in Practice, ed. W. Gilks, S. Richardson and D. J. Spiegelhalter, 145--161. London: Chapman and Hall.
• 14: Peter W. Glynn, Problems in Bayesian analysis of stochastic simulation, Proceedings of the 18th conference on Winter simulation, p.376-379, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318462]
• 15: Guedes Soares, C., A. P. Teixeira. 2001. Risk assessment in maritime transportation. Reliability Engineering and System Safety 74(3): 299--309.
• 16: Hara, K., S. Nakamura. 1995. A comprehensive assessment system for the maritime traffic environment. Safety Science 19(2--3): 203--215.
• 17: Hofer, E. 1996. When to separate uncertainties and when not to separate. Reliability Engineering and System Safety 54: 113--118.
• 18: Hora, S. 1996. Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliability Engineering and System Safety 54:217--223.
• 19: Kass, R., A. Raftery. 1995. Bayes Factors. Journal of the American Statistical Association 90(430): 773--795.
• 20: Kite-Powell, H. L., D. Jin, N. M. Patrikalis, J. Jebsen, V. Papakonstantinou. 1996. Formulation of a Model for Ship Transit Risk. MIT Sea Grant Technical Report, 96--19. Cambridge, MA.
• 21: Averill M. Law , David M. Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1999
• 22: Merrick, J. R. W., J. R. van Dorp, J. Harrald, T. Mazzuchi, J. Spahn, M. Grabowski. 2000. A systems approach to managing oil transportation risk in Prince William Sound. Systems Engineering3(3): 128--142.
• 23: Jason R. W. Merrick , J. René van Dorp , Thomas Mazzuchi , John R. Harrald , John E. Spahn , Martha Grabowski, The Prince William Sound Risk Assessment, Interfaces, v.32 n.6, p.25-40, November 2002 [doi>10.1287/inte.32.6.25.6474]
• 24: Merrick, J., J. R. van Dorp, J. P. Blackford, G. L. Shaw, J. Harrald, T. Mazzuchi. 2003. A traffic density analysis of proposed ferry service expansion in San Francisco Bay using a maritime simulation model. To appear in Reliability Engineering and System Safety.
• 25: Paté-Cornell, M. E. 1996. Uncertainties in risk analysis: six levels of treatment. Reliability Engineering and System Safety 54(2--3): 95--111.
• 26: Roeleven, D., M. Kok, H. L. Stipdonk, W. A. de Vries. 1995. Inland waterway transport: Modeling the probabilities of accidents. Safety Science 19(2--3): 191--202.
• 27: Slob, W. 1998. Determination of risks on inland waterways. Journal of Hazardous Materials 61(1--3): 363--370.
• 28: Spiegelhalter, D., A. Thomas, N. Best, W. Gilks. 1996. BUGS: Bayesian inference Using Gibbs Sampling, Version 0.5. MRC Biostatistics Unit: Cambridge, UK.
• 29: Spiegelhalter, D. J., N. G. Best, B. P. Carlin, A. van der Linde. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B 64(4): 583--639.
• 30: Trbojevic, V. M., B. J. Carr. 2000. Risk based methodology for safety improvements in ports. Journal of Hazardous Materials 71(1--3): 467--480.
• 31: van Dorp, J. R., J. R. W. Merrick, J. Harrald, T. Mazzuchi, M. Grabowski. 2001. A Risk Management Procedure for the Washington State Ferries. Risk Analysis21(1): 127--142.
• 32: Wang, J. 2000. A subjective modeling tool applied to formal ship safety assessment. Ocean Engineering 27(10): 1019--1035.