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Emergency facility location under random network damage: Insights from the Istanbul case

机译:网络受到随机破坏时的应急设施位置:伊斯坦布尔案的见解

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Damage to infrastructure, especially to highways and roads, adversely affects accessibility to disaster areas. Predicting accessibility to demand points from the supply points by a systematic model would lead to more effective emergency facility location decisions. To this effect, we model the spatial impact of the disaster on network links by random failures with dependency such that failure of a link induces failure of nearby links that are structurally more vulnerable. For each demand point, a set of alternative paths is generated from each potential supply point so that the shortest surviving path will be used for relief transportation after the disaster. The objective is to maximize the expected demand coverage within a specified distance over all possible network realizations. To overcome the computational difficulty caused by extremely large number of possible outcomes, we propose a tabu search heuristic that evaluates candidate solutions over a sample of network scenarios. The scenario generation algorithm that represents the proposed distance and vulnerability based failure model is the main contribution of our study. The tabu search algorithm is applied to Istanbul earthquake preparedness case with a detailed analysis comparing solutions found in no link failure, independent link failure, and dependent link failure cases. The results show that incorporating dependent link failures to the model improves the covered demand percentages significantly. (C) 2014 Elsevier Ltd. All rights reserved.
机译:基础设施的损害,特别是对公路和道路的损害,严重影响了灾区的可达性。通过系统的模型预测从供应点到需求点的可访问性将导致更有效的应急设施位置决策。为此,我们通过具有依赖关系的随机故障对灾难对网络链路的空间影响进行建模,以使链路故障会导致结构上更脆弱的附近链路发生故障。对于每个需求点,将从每个潜在的供应点生成一组替代路径,以便将最短的幸存路径用于灾后的救灾运输。目的是在所有可能的网络实现中的指定距离内最大化预期的需求覆盖范围。为了克服由大量可能的结果导致的计算困难,我们提出了一种禁忌搜索试探法,该试探法评估网络场景样本中的候选解决方案。代表提出的基于距离和脆弱性的故障模型的场景生成算法是我们研究的主要贡献。禁忌搜索算法应用于伊斯坦布尔地震准备情况,并进行了详细分析,比较了在没有链路故障,独立链路故障和相关链路故障情况下发现的解决方案。结果表明,将相关的链路故障合并到模型中可以显着提高覆盖的需求百分比。 (C)2014 Elsevier Ltd.保留所有权利。

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