救災(zāi)物資的需求推演及調(diào)度研究
發(fā)布時間：2018-09-14 09:37
【摘要】：近年自然災(zāi)害頻發(fā),如何提高救援效率成為應(yīng)急物流中的重要問題。本文以此為背景,主要研究了救災(zāi)物資的分類及分級、救災(zāi)物資需求的推演以及救災(zāi)物資的協(xié)同調(diào)配機制。 救災(zāi)物資分類及分級研究基于權(quán)變理論的相似性,將組織行為學(xué)中的費德勒權(quán)變模型遷移到災(zāi)前考慮經(jīng)濟成本的情況下應(yīng)急物資的定性分類中,指導(dǎo)災(zāi)前救災(zāi)物資的準備�？紤]到自然災(zāi)害發(fā)生時救援活動及救災(zāi)物資的弱經(jīng)濟性,建立了用灰色白化權(quán)函數(shù)聚類分析的評估體系,以高效地解決應(yīng)急物資分級問題。進一步基于某一地區(qū)所有可能發(fā)生的災(zāi)種對應(yīng)急物資的重要程度進行了分級。 針對大型地震發(fā)生后傷亡人數(shù)和救災(zāi)物資需求的預(yù)測及推演問題,建立“三步法”推演模型：用灰色生成序列改進BP神經(jīng)網(wǎng)絡(luò),建立預(yù)測總傷亡人數(shù)灰色BP神經(jīng)網(wǎng)絡(luò)；應(yīng)用Mistcherlich模型動態(tài)推演每天的傷亡人數(shù)；在此基礎(chǔ)上,構(gòu)建基于時間和庫存管理的救災(zāi)物資需求實時推演模型。針對BP神經(jīng)網(wǎng)絡(luò)的改進和救災(zāi)物資實時推演模型進行了重點闡述。模型基于每日傷亡人口的動態(tài)預(yù)測和前線的傷亡數(shù)據(jù)更新,對每個供給間隔期運送的物資量進行實時推演。 基于救災(zāi)物資需求的推演,以最快響應(yīng)、最少損失和最低成本為目標函數(shù),建立了基于確定物資需求的多種物資、多出救點、多受災(zāi)點的復(fù)雜應(yīng)急物資調(diào)度模型。提出一種改進的調(diào)配機制,即同一救援范圍內(nèi)的出救點之間可以互相調(diào)配物資的機制,并分別選擇出救區(qū)域和受災(zāi)區(qū)域的樞紐中心,建立軸輻式網(wǎng)絡(luò),不僅緩解了運輸壓力,而且由于樞紐中心的存在,保證了救災(zāi)物資供需可控,保證救援活動有序進行。
[Abstract]:In recent years, natural disasters occur frequently, how to improve rescue efficiency becomes an important problem in emergency logistics. Based on this background, this paper mainly studies the classification and classification of disaster relief materials, the deduction of disaster relief material demand and the coordination mechanism of disaster relief materials. Based on the similarity of contingency theory, Federer's contingency model in organizational behavior is transferred to the qualitative classification of emergency materials considering the economic cost before the disaster, which can guide the preparation of disaster relief materials. Considering the weak economy of relief activities and relief materials during natural disasters, an evaluation system based on grey whitening weight function cluster analysis is established to efficiently solve the problem of emergency material classification. Further classification is made based on the importance of all possible disasters in an area to emergency supplies. In view of the prediction and deduction of casualties and the demand for disaster relief materials after a large-scale earthquake, a "three-step" model is established: the grey BP neural network is established to predict the total number of casualties by improving the BP neural network with grey generating sequence; Based on the Mistcherlich model, the real-time model of disaster relief material demand is built based on time and inventory management. The improvement of BP neural network and the real-time deduction model of disaster relief materials are discussed in detail. Based on the dynamic prediction of the daily casualty population and the update of the casualty data in the front line, the model is used to estimate the quantity of goods transported in each period of the supply period in real time. Based on the derivation of the demand for disaster relief materials and taking the fastest response, minimum loss and minimum cost as the objective function, a complex emergency material scheduling model based on the determination of the material demand, multiple rescue points and multiple disaster points is established. In this paper, an improved deployment mechanism is put forward, that is, the mechanism of distributing materials among the rescue points in the same rescue area, and selecting the hub center of the rescue area and the affected area, respectively, and establishing the axis-radial network, which not only alleviates the transport pressure, Because of the existence of the hub center, the supply and demand of relief materials are controlled and the rescue activities are carried out in an orderly manner.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：D632.5;D63
本文編號：2242338
[Abstract]:In recent years, natural disasters occur frequently, how to improve rescue efficiency becomes an important problem in emergency logistics. Based on this background, this paper mainly studies the classification and classification of disaster relief materials, the deduction of disaster relief material demand and the coordination mechanism of disaster relief materials. Based on the similarity of contingency theory, Federer's contingency model in organizational behavior is transferred to the qualitative classification of emergency materials considering the economic cost before the disaster, which can guide the preparation of disaster relief materials. Considering the weak economy of relief activities and relief materials during natural disasters, an evaluation system based on grey whitening weight function cluster analysis is established to efficiently solve the problem of emergency material classification. Further classification is made based on the importance of all possible disasters in an area to emergency supplies. In view of the prediction and deduction of casualties and the demand for disaster relief materials after a large-scale earthquake, a "three-step" model is established: the grey BP neural network is established to predict the total number of casualties by improving the BP neural network with grey generating sequence; Based on the Mistcherlich model, the real-time model of disaster relief material demand is built based on time and inventory management. The improvement of BP neural network and the real-time deduction model of disaster relief materials are discussed in detail. Based on the dynamic prediction of the daily casualty population and the update of the casualty data in the front line, the model is used to estimate the quantity of goods transported in each period of the supply period in real time. Based on the derivation of the demand for disaster relief materials and taking the fastest response, minimum loss and minimum cost as the objective function, a complex emergency material scheduling model based on the determination of the material demand, multiple rescue points and multiple disaster points is established. In this paper, an improved deployment mechanism is put forward, that is, the mechanism of distributing materials among the rescue points in the same rescue area, and selecting the hub center of the rescue area and the affected area, respectively, and establishing the axis-radial network, which not only alleviates the transport pressure, Because of the existence of the hub center, the supply and demand of relief materials are controlled and the rescue activities are carried out in an orderly manner.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：D632.5;D63
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，本文編號：2242338
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