大范围洪涝灾害分层协同备灾储备库选址优化研究

Study on hierarchical and coordinated optimization of site selection for large-scale flood disaster preparedness material reserves

摘要

【目的】针对洪涝灾害情景下应急物资储备库选址过程中需求点空间分布复杂、异质性显著及资源配置效率不足的问题,提出了兼顾需求特征分区与多层级配置的储备库选址优化方法。【方法】从危险度、暴露度与可达性三个维度选取指标构建需求点多维特征向量,经标准化处理后,利用自组织映射神经网络实现高维特征的拓扑映射,基于神经元权重空间计算类内平方误差,通过肘部法则获得需求簇类别。建立初级储备库选址模型,在子区域上以需求权重与运输距离乘积最小为目标进行优化;在全局上构建高级储备库选址模型进行优化。【结果】结果显示:需求点最优划分类别数为34,基于34个子区域的分区结果,初级储备库实现了分区内需求的有效覆盖;在此基础上,通过全局优化确定8个高级储备库实现资源配置的协同优化。与传统方法相比,优化模型建设成本降低约16.1%,加权平均响应时间缩短约17.1%。【结论】结果表明:所构建方法能够在刻画洪涝灾害需求点多维异质性的基础上,实现分区驱动的两级储备库优化配置;通过“SOM分区—局部优化—全局协调”的方法框架,可显著提升应急资源配置的空间适应性与系统效率。

Abstract

[Objective] To address the complex spatial distribution, significant heterogeneity of demand points, and insufficient efficiency of resource allocation in emergency material reserve location under flood disaster scenarios, an optimization approach integrating demand-based zoning and hierarchical facility allocation is developed. [Methods] Multi-dimensional feature vectors of demand points were constructed from three dimensions, including hazard, exposure, and accessibility. After normalization, a self-organizing map( SOM) neural network was employed to achieve topological mapping of high-dimensional features. The within-cluster sum of squared errors was calculated based on the neuron weight space, and the optimal number of demand clusters was determined using the elbow method. At the subregional level, a primary reserve location model was established, with the objective of minimizing the product of demand weight and transportation distance. At the global level, a higher-level reserve location model was constructed for optimization. [Results] The results showed that the optimal number of demand clusters was determined to be 34. Based on the classification result of the 34 subregions, primary reserves achieved effective coverage of intraregional demand. On this basis, eight higher-level reserves were identified through global optimization to enable coordinated resource allocation. Compared with conventional method, construction cost was reduced by approximately 16. 1%, and the weighted average response time was shortened by approximately 17. 1%. [Conclusion] The results indicate that the proposed method can effectively characterize the multidimensional heterogeneity of demand points under flood disaster scenarios and achieve zoning-driven two-level optimization of reserve allocation. Through the framework of “SOM-based zoning-local optimization-global coordination,” the spatial adaptability and system efficiency of emergency resource allocation can be significantly improved.

关键词

大范围洪涝灾害 / 储备库选址 / 聚类分析 / 多目标优化 / 需求异质性 / 自组织映射 / 应急物资储备 / 影响因素

Key words

large-scale flood disaster / reserve site selection / cluster analysis / multi-objective optimization / demand heterogeneity / self-organizing map / emergency material reserve / influencing factors

基金资助

国家重点研发计划(2023YFC3006504)

北京市自然科学基金项目(8242003)