【作者】 郭金芬；

【导师】 周刚；

【作者基本信息】 天津大学 ， 工业工程， 2012， 硕士

【摘要】 近年来,汶川8.0级大地震、青海玉树7.1级地震以及日本海域9.0级特大地震等大型地震的发生给社会造成了重大的人员伤亡和财产损失,引起了广大人民的关注。为了最大限度地减少人员伤亡,震后需要及时展开救援工作以及保障灾民基本生活。大型地震往往会造成灾区基础设施破坏、通讯中断,使得信息流畅不通,因此,很难迅速收集到灾民所需应急物资的准确信息,这就给有效开展应急物资的筹措和配送工作带来很大障碍。因此,如何在震后第一时间内获取灾民所需应急物资的信息,这成为了减灾救灾工作系统中需要解决的一个问题。本文首先分析总结了震灾中应急物资的需求特征和需求分析内容,并将研究范围限定在地震发生初期应急物资需求数量的预测上。基于地震中应急物资需求的突发性、不确定性、供方主导性、弱经济性等特性,决定了不能运用传统的数理统计预测模型对应急物资的需求进行预测分析。经过探索和分析,本文构建了一种动态的即时估测方法。该方法可分为两个阶段:第一阶段中,通过能够快速和便于收集到的信息,运用主成分BP神经网络模型对震灾中各受灾点的人员伤亡数据进行预测;第二阶段,在各受灾点人员伤亡预测数据的基础上,运用商业物流中的库存管理知识,对这种震灾中需求未知情况下应急物资的供给量进行估算。对于主成分BP神经网络地震伤亡预测模型,本文从影响地震伤亡的众多参数中选取了能够在震后第一时间直接获取或者可通过快速评估方法获取的震级、地震发生时间、破坏烈度、受灾人口等8个参数,并通过中国地震信息网、图书《中国震例》和各地统计年鉴等资料等,收集了历年来几次大型地震的相关数据作为样本。为避免参数具有相关性,本文运用主成分析法对其进行降维,然后构建了三层BP神经网络模型,对降维后的数据进行训练,经测试样本验证,该模型预测的数据能够满足误差要求。在应急物资需求量的估算方法中,本文根据应急物资的需求特性,运用安全库存理论建立了饮食类、御寒物资类和基本药品类需求量的估算公式。最后,本文运用所构建的模型和方法对青海玉树地震初期玉树州境内各受灾点所需的压缩饼干、饮用水、帐篷、消炎药等应急物资进行了估算。更多还原

【Abstract】 In recent years, the 8.0 magnitude Earthquake in Wenchuan, the 7.1 magnitude earthquake in Yushu and 9.0 magnitude Earthquake in Japan have caused heavy casualties and severe property damage. A lot of people paid attention to them. To minimize the casualties resulted from earthquakes, the rescue work should begin as soon as possible, and the basic life of victims should be ensured after earthquakes. Usually, large-scale earthquakes resulted in great damage to infrastructure in disaster areas, which makes the information exchange blocked, so the information of emergency material demand cannot be received. This brings many problems to raise, distribute and supply emergency materials. Therefore, how to get information about emergency materials needed by people in disaster the first time after earthquakes has became a problem which needs be solved for rescue and relief work.In this paper, firstly, the characteristic and contents of emergency material demand are analyzed and studied, and the studying coverage is limited to the quantities of emergency materials in early time after earthquakes. Based the characteristics of sudden, uncertainty, supplier-controlled and the weak economy in emergency demand, the regular forecast models are not used step by step for forecasting emergency materials demand. Through research and analysis, a dynamic real-time estimation method has been made in this paper. The method can be divided into two stages. In the first stage, based on the information which can be collected quickly and easily, the principal component analysis and BP neural network model is constructed to predict the casualties in earthquakes. In the second stage, the estimate data of casualties and the inventory management knowledge are made used of to estimate the quantity of materials under the situation of unknown demand.As for the principal component analysis and BP neural network model for casualties estimation, firstly, the eight parameters including the magnitude, time, intensity, etc. are chose from many parameters which influenced the casualties, and this eight parameters can be obtained directly or by the rapid assessment methods the first time after earthquakes. Secondly, the information about several large-scale earthquakes in history is obtained form China Earthquake Information Net, books Database of History Earthquakes in China and Statistical Yearbooks as samples. To avoid correlation among parameters selected, the data dimension is reduced by principle component method. Then a three layer BP neural network model is constructed to train the data in samples. Through that the simulation data and actual data in test samples has been compared, the data simulated by the model can meet the error requirement. For the emergency material estimation method, according to its characteristics, the safety stock theory is employed to set up formulas for estimate the food, clothes and basic drugs used by people in disasters.Finally, the method structured in this paper is used to estimate the demand of convenient food, bottled water , tents and anti-inflammatory in the 7.1 magnitude earthquake which occurred in Yushu, Qinghai.更多还原