Abstract
Disruption of network facilities (nodes or arcs) resulting in loss of system functionality is of particular concern in the operation of critical network infrastructures. In preparing for events such as natural disasters, accidents, and/or sabotage, numerous disruption possibilities exist for any network and efforts to safeguard against the most destructive of those are often sought. Although, many approaches exist for identifying network vulnerabilities, mathematical programming and simulation are typically used as exploratory approaches for uncovering potentially important disruption scenarios. Mathematical programming approaches can identify bounds on network vulnerability while simulation can provide important insights to complement these bounds, as well as provide insights in cases where mathematical programming is not practical. This research presents a simulation methodology for evaluating network vulnerability to disruption. A case study illustrates the complexities associated with planning for such events.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 307-321 |
| Number of pages | 15 |
| Journal | Annals of Regional Science |
| Volume | 43 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2009 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Social Sciences(all)
- Environmental Science(all)
Cite this
Exploring the vulnerability of network infrastructure to disruption. / Matisziw, Timothy C.; Murray, Alan T.; Grubesic, Anthony.
In: Annals of Regional Science, Vol. 43, No. 2, 2009, p. 307-321.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Exploring the vulnerability of network infrastructure to disruption
AU - Matisziw, Timothy C.
AU - Murray, Alan T.
AU - Grubesic, Anthony
PY - 2009
Y1 - 2009
N2 - Disruption of network facilities (nodes or arcs) resulting in loss of system functionality is of particular concern in the operation of critical network infrastructures. In preparing for events such as natural disasters, accidents, and/or sabotage, numerous disruption possibilities exist for any network and efforts to safeguard against the most destructive of those are often sought. Although, many approaches exist for identifying network vulnerabilities, mathematical programming and simulation are typically used as exploratory approaches for uncovering potentially important disruption scenarios. Mathematical programming approaches can identify bounds on network vulnerability while simulation can provide important insights to complement these bounds, as well as provide insights in cases where mathematical programming is not practical. This research presents a simulation methodology for evaluating network vulnerability to disruption. A case study illustrates the complexities associated with planning for such events.
AB - Disruption of network facilities (nodes or arcs) resulting in loss of system functionality is of particular concern in the operation of critical network infrastructures. In preparing for events such as natural disasters, accidents, and/or sabotage, numerous disruption possibilities exist for any network and efforts to safeguard against the most destructive of those are often sought. Although, many approaches exist for identifying network vulnerabilities, mathematical programming and simulation are typically used as exploratory approaches for uncovering potentially important disruption scenarios. Mathematical programming approaches can identify bounds on network vulnerability while simulation can provide important insights to complement these bounds, as well as provide insights in cases where mathematical programming is not practical. This research presents a simulation methodology for evaluating network vulnerability to disruption. A case study illustrates the complexities associated with planning for such events.
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UR - http://www.scopus.com/inward/citedby.url?scp=67349263869&partnerID=8YFLogxK
U2 - 10.1007/s00168-008-0235-x
DO - 10.1007/s00168-008-0235-x
M3 - Article
VL - 43
SP - 307
EP - 321
JO - Annals of Regional Science
JF - Annals of Regional Science
SN - 0570-1864
IS - 2
ER -