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Risk based facility location by using fault tree analysis in disaster management

机译:在灾难管理中使用故障树分析进行基于风险的设施定位

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摘要

Determining the locations of facilities for prepositioning supplies to be used during a disaster is a strategic decision that directly affects the success of disaster response operations. Locating such facilities close to the disaster-prone areas is of utmost importance to minimize response time. However, this is also risky because the facility may be disrupted and hence may not support the demand point(s). In this study, we develop an optimization model that minimizes the risk that a demand point may be exposed to because it is not supported by the located facilities. The purpose is to choose the locations such that a reliable facility network to support the demand points is constructed. The risk for a demand point is calculated as the multiplication of the (probability of the) threat (e.g., earthquake), the vulnerability of the demand point (the probability that it is not supported by the facilities), and consequence (value or possible loss at the demand point due to threat). The vulnerability of a demand point is computed by using fault tree analysis and incorporated into the optimization model innovatively. To our knowledge, this paper is the first to use such an approach. The resulting non-linear integer program is linearized and solved as a linear integer program. The locations produced by the proposed model are compared to those produced by the p-center model with respect to risk value, coverage distance, and covered population by using several test problems. The model is also applied in a real problem. The results indicate that taking the risk into account explicitly may create significant differences in the risk levels.
机译:确定用于在灾难期间预先配置要使用的物品的设施的位置是一项战略决策,它直接影响灾难响应操作的成功。将此类设施放置在易受灾地区附近对于最大限度地缩短响应时间至关重要。然而,这也是有风险的,因为设施可能被破坏,因此可能不支持需求点。在这项研究中,我们开发了一个优化模型,该模型可以最大程度地降低因需求点不受所设设施支持而可能面临的需求点风险。目的是选择位置,以便构建可支持需求点的可靠设施网络。需求点的风险计算为威胁(例如地震)(概率),需求点的脆弱性(设施不支持的可能性)和后果(价值或可能性)的乘积由于威胁在需求点造成的损失)。通过使用故障树分析来计算需求点的脆弱性,并以创新的方式将其纳入优化模型。据我们所知,本文是第一个使用这种方法的。将生成的非线性整数程序线性化并求解为线性整数程序。通过使用一些测试问题,将所提出的模型产生的位置与p中心模型产生的位置在风险值,覆盖距离和覆盖人口方面进行了比较。该模型也适用于实际问题。结果表明,明确考虑风险可能会在风险水平上产生重大差异。

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