A combination of Monte Carlo simulation and cellular automata for computing the availability of complex network systems

Zio, Enrico; Podofillini, Luca; Zille, Valérie

doi:10.1016/j.ress.2004.12.002

Cited by 47 publications

(24 citation statements)

References 15 publications

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“…Similar to Risk Achievement Worth (Zio et al, 2006), BCAW measures the amount that the business continuity metrics would improve if a business continuity measure could reach its ideal conditions. In this paper, we use the difference between the ideal and nominal scenarios for the evaluation of BCAW:…”

Section: Importance Measures For Business Continuitymentioning

confidence: 99%

Interval-valued importance measures for business continuity management

Zeng

Zio

2017

Safety and Reliability – Theory and Applications

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Section: Importance Measures For Business Continuitymentioning

confidence: 99%

Interval-valued importance measures for business continuity management

Zeng

Zio

2017

Safety and Reliability – Theory and Applications

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“…Many people have developed different kinds of Monte Carlo techniques to estimate the system reliability from the graph of a network (Kamat & Riley, 1975, 1976. Researchers have only applied MCS to estimate network reliability by focusing on how to design sampling plan to reduce the variance in terms of the known MP/MC (Rocco et al, 2002(Rocco et al, , 2005Fishman, 1986;Kubat, 1989;Landers et al, 1991;Lin & Donaghey, 1993;Ramirez-Má rquez & Coit, 2005, 2007Zio et al, 2006). However, to get all MP or MC information from the network is a NP-hard problem (Colbourn, 1987;Garey & Johnson, 1979).…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

“…(4) The operation time of the computer get faster so that the manipulating time of MCS process decreases (Wang & Pham, 1997). As a result, MCS algorithm has became one of the efficient and optimal approaches for estimating network reliability (Rocco & Moreno, 2002;Rocco & Zio, 2005;Easton & Wong, 1980;Fishman, 1986;Kamat & Riley, 1975, 1976Kubat, 1989;Landers et al, 1991;Lin & Donaghey, 1993;Ramirez-Má rquez & Coit, 2005, 2007Wang & Pham, 1997;Yeh, 1996Yeh, , 1998Yeh, , 2003Yeh, , 2004aYeh, ,b, 2007Yeh & Lin, 2009;Yeh, Lin, & Lin, 2007;Zio, Podofillini, & Zille, 2006).…”

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confidence: 99%

Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability

Yeh

Lin

Chung

2010

Expert Systems with Applications

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“…There is counter Uk-working which tracks the number of times the element is not failed (1 -Uk-fail) and Uk-working-system-fail for when the element is working but the system is failed. Ukj, U+ykj, U-ykj are therefore given by the following Equations (7) through (9): (Zio, Podofillini, and Zille, 2006) In our case study (presented later), we calculated all four IMs listed above, however the first thing we noticed about our final rankings was that two the results using two IMs yielded the same ranking. These two (RRW and FV) gave us a sanity check since, as shown in eq.…”

Section: Importance Measuresmentioning

confidence: 99%

Identification of critical locations across multiple infrastructures for terrorist actions

Patterson

Apostolakis

2007

Reliability Engineering & System Safety

144

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This paper discusses a possible approach to ranking geographic regions that can influence multiple infrastructures. Once ranked, decision makers can determine whether these regions are critical locations based on their susceptibility to terrorist acts. We identify these locations by calculating a value for a geographic region which represents the combined values to the decision makers of all the infrastructures crossing through that region. These values, as well as the size of the geographic regions, are conditional on a minor destructive threat of a given size, e.g,. a bomb that can affect objects within 15 feet of it.This approach first requires an assessment of the users of the system. During this assessment, each user is assigned a performance index (PI) based on the disutility of the loss of each infrastructure's resource via multi-attribute utility theory (MAUT). A Monte Carlo network analysis is then performed to develop importance measures (IM) for the elements of each infrastructure for their ability to service each user. We combine the IMs with the user PIs to a value that we call valued worth (VW) for each infrastructure's elements independently. Then we use spatial analysis techniques within a Geographic Information System (GIS) to combine the VWs of each infrastructure's elements in a geographic area, conditional on the threat, into a total value we call geographic valued worth (GVW). The GVW is graphically displayed in the GIS system in a color scheme that shows the numerical ranking of these geographic areas. The map and rankings are then submitted to the decision makers to better allocate anti-terrorism resources.A case study of this methodology is preformed on the Massachusetts Institute of Technology's (MIT) campus. The results of the study show how the methodology can bring attention to areas that may be ignored through individual infrastructure analysis. The intersections of major infrastructures on the campus prove to be of the most importance to the stakeholders of the campus.

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A combination of Monte Carlo simulation and cellular automata for computing the availability of complex network systems

Cited by 47 publications

References 15 publications

Interval-valued importance measures for business continuity management

Interval-valued importance measures for business continuity management

Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability

Identification of critical locations across multiple infrastructures for terrorist actions

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