2011
DOI: 10.1061/(asce)is.1943-555x.0000039
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Postearthquake Recovery of a Water Distribution System: Discrete Event Simulation Using Colored Petri Nets

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Cited by 56 publications
(29 citation statements)
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“…Models that simulate the failure and restoration of complex systems can use these estimates to generate realistic scenarios (Luna et al 2011). Understanding and predicting the rate of recovery of disabled or inoperable infrastructure and economic sectors can help decision makers examine the tradeoffs between the cost of protection and the consequences of different disruptions (Tsang et al 2002).…”
Section: Resultsmentioning
confidence: 99%
“…Models that simulate the failure and restoration of complex systems can use these estimates to generate realistic scenarios (Luna et al 2011). Understanding and predicting the rate of recovery of disabled or inoperable infrastructure and economic sectors can help decision makers examine the tradeoffs between the cost of protection and the consequences of different disruptions (Tsang et al 2002).…”
Section: Resultsmentioning
confidence: 99%
“…Cagnan and Davidson [25] utilized this method to simulate the post-earthquake restoration process for electric power systems. Moreover, Luna et al [26] applied discrete event simulation to model the post-earthquake recovery of water distribution system. Huling and Miles [27] employed SimPy to simulate the disaster recovery of buildings.…”
Section: Modeling the Post-disaster Recoverymentioning
confidence: 99%
“…A detailed case study of the 1994 Northridge, CA earthquake is used to illustrate the model's ability to estimate system restoration time. Luna et al [23] use colored Petri nets and discrete event simulation to model and study water system restoration. The methods are illustrated on the trunk network of the Tokyo water distribution system.…”
Section: Restoration Management After Disastermentioning
confidence: 99%
“…The first MIP (Phase-I MIP) computes an optimal solution to the linearized model in which we use T 1 linear pieces for equations (22) and (23), and T 2 linear pieces for equations (27) and (28) where T 1 and T 2 are relatively small. Keeping T 1 and T 2 small results in a small MIP that can be easily solved using commercial solvers such as CPLEX.…”
Section: Objective Function and Computational Characteristicsmentioning
confidence: 99%
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