Object-oriented model of the seismic vulnerability of the fuel distribution network in coastal British Columbia

Costa, Rodrigo; Haukaas, Terje; Chang, Stephanie E.; Dowlatabadi, Hadi

doi:10.1016/j.ress.2019.02.006

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…Although it is difficult to collect precise proprietary data, we recognize the call for research using field data, with all its "noise, dirt, and missing elements" by Besiou and Van Wassenhove (2020). Similar to Biller et al (2019) and Costa et al (2019) we rely on public sources, industry insights, visits to terminals in the US, and validation from industry practitioners to identify parameter values.…”

Section: Numerical Analysismentioning

confidence: 99%

“…Existing studies focus on upstream distribution (Sabuncuoglu and Hatip, 2005; Cafaro et al., 2010), a single terminal facility (Reis et al., 2017), outlining system vulnerabilities (Costa et al., 2019), or infrastructure improvement (DeCorla-Souza, 2018). Only a few of them analyze the effects of improving terminal bay processes and fleet size on the distribution system’s performance (e.g., Reis et al., 2017; DeCorla-Souza, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similar to Biller et al. (2019) and Costa et al. (2019) we rely on public sources, industry insights, visits to terminals in the US, and validation from industry practitioners to identify parameter values.…”

Section: Numerical Analysismentioning

confidence: 99%

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Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

Rana,

Russell,

Boutilier

et al. 2024

Production and Operations Management

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Fuel is an essential commodity in society with an amplified role during disasters. Disasters often cause a spike in demand for fuel and impose physical and operational constraints on fuel distribution. Accordingly, public and private sector stakeholders seek disaster preparedness and response interventions to ensure an adequate supply of fuel for emergency activities. We develop models to quantify downstream operational flow capacity, i.e., the volume of fuel that can be distributed from bulk storage terminals to retail gas stations via tanker trucks. We do this for both steady state conditions and non-steady state conditions during disasters. The operational flow capacity measures can serve as inputs to optimal relief distribution and resource allocation problems, as well as enable a cost-benefit assessment of interventions aimed at increasing system capacity. We identify the best interventions for each type of bulk storage terminal structure and determine how to prioritize bulk storage terminals for each type of intervention when resources are limited. As the downstream fuel distribution system from bulk storage terminals to retail gas stations is similar across regions, our methodology and insights are generalizable not only within the US but also in countries with comparable distribution systems. To demonstrate impact on practice, we present a case study drawn from the application of our methodology to support US federal disaster preparedness. More generally, we contribute to system level understanding of multi-server tandem cyclic queues with time-limited customers found in relief distribution systems, which is often overlooked in the disaster management literature.

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Section: Numerical Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

Rana,

Russell,

Boutilier

et al. 2024

Production and Operations Management

View full text Add to dashboard Cite

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“…For example, Laucelli and Giustolisi (2015) developed the vulnerability model for the water distribution network, where the material of pipeline, type of joint, and diameter of pipeline were considered as influential factors. In addition to these three indices, Costa et al (2019) also incorporated the length of the pipeline into their model for the fuel distribution network. Lanzano et al (2013) suggested the type of fluid (e.g., water, natural gas, oil) may affect the parameters of vulnerability models.…”

Section: Vulnerability Model Of Assetsmentioning

confidence: 99%

System vulnerability modeling and assessment in seismic-prone areas using machine learning

Chen¹

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Earthquakes have caused great social and economic losses to human societies in recent years. Vulnerability modeling and assessment are important measurements for seismic risk mitigation. Conventional seismic vulnerability analysis frameworks have limitations such as subjectivity, low capacity of index information, or high computation cost. The motivation of this thesis is to overcome these drawbacks by employing machine learning techniques. In this thesis, a machine learning-based seismic vulnerability management framework is proposed. The proposed framework is composed of four objectives: Objective 1 aims to assess the regional seismic vulnerability with the information fusion approach. Objective 2 aims to assess the building vulnerability by XGBoost models learned from earthquake building damage data. Objective 3 aims to predict the casualty rate and economic loss of earthquake disaster areas with AutoML models. Objective 4 aims to explore the effective emergency response plans of hospital networks by Multi-objective optimization.12

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The effect of resource constraints on housing recovery simulations

Costa

Haukaas

2021

International Journal of Disaster Risk Reduction

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Object-oriented model of the seismic vulnerability of the fuel distribution network in coastal British Columbia

Cited by 10 publications

References 17 publications

Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

System vulnerability modeling and assessment in seismic-prone areas using machine learning

The effect of resource constraints on housing recovery simulations

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