巨灾救援链系统中的灾区聚类与排序问题研究——以汶川地震为例

Clustering of Earthquake Stricken Areas and Sequencing of Demand Degrees in Disaster Relief Chain ——A Case Study on Wenchuan Earthquake

重大灾害发生时,如何在最短的时间内根据受灾情况进行物流规划是巨灾救援链管理研究所面临的难题之一。而根据受灾程度和物资需求属性进行准确的灾区分级与排序则是应急物流取得成功的重要前提。根据自组织神经网络方法建立了一个灾区聚类模型,并以汶川地震为例进行了仿真分析;采用TOPSIS多目标决策分析解决了各受灾区救援物资分配的优先级排序。

Logistics planning based on the disaster situation in the shortest time is one of the important problems in the disaster relief chain management. Accurate disaster area grading and sorting according to the demands property is an important prerequisite for the success of the emergency logistics. This paper, based on self-organizing neural network approach to establish a disaster area clustering model and simulation analysis of Wenchuan earth- quake; Next, we use the TOPSIS multi-objective decision analysis to solve prioritize the distribution of relief supplies in all affected areas. This research provides an important analytical tool for emergency logistics planning.