本文選題：應(yīng)急物資 + 需求預(yù)測(cè)��； 參考：《西安科技大學(xué)》2014年碩士論文

【摘要】：近年來(lái)自然災(zāi)害頻發(fā)，給人們的生命財(cái)產(chǎn)安全和社會(huì)經(jīng)濟(jì)發(fā)展帶來(lái)了極大威脅，建立高效快速的應(yīng)急救援系統(tǒng)迫在眉睫。應(yīng)急物資的及時(shí)有效配送是救援工作的重中之重，而對(duì)應(yīng)急物資需求的準(zhǔn)確預(yù)測(cè)是保證配送工作順利進(jìn)行的前提。本文圍繞應(yīng)急救援工作，，展開(kāi)了應(yīng)急物資需求預(yù)測(cè)和應(yīng)急物資配送問(wèn)題的研究。 首先，利用間接的方法預(yù)測(cè)災(zāi)區(qū)應(yīng)急物資需求量。BP神經(jīng)網(wǎng)絡(luò)算法是預(yù)測(cè)災(zāi)區(qū)人員傷亡時(shí)的熱點(diǎn)算法，針對(duì)標(biāo)準(zhǔn)BP神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)效率低、易陷入局部最優(yōu)的缺點(diǎn)，運(yùn)用附加動(dòng)量和自適應(yīng)調(diào)整學(xué)習(xí)率相結(jié)合的方法對(duì)其進(jìn)行改進(jìn)；建立了基于BP神經(jīng)網(wǎng)絡(luò)的災(zāi)區(qū)人員傷亡預(yù)測(cè)模型，并對(duì)15次大型地震中的傷亡人數(shù)進(jìn)行預(yù)測(cè)，驗(yàn)證了預(yù)測(cè)模型的合理性和算法的有效性；之后，在建立應(yīng)急物資需求量預(yù)測(cè)模型的基礎(chǔ)上，估算汶川地震中帳篷的需求量。其次，基于應(yīng)急物資配送問(wèn)題的特點(diǎn)，構(gòu)建了以滿(mǎn)足災(zāi)區(qū)需求為前提，總配送時(shí)間最短的應(yīng)急物資配送問(wèn)題的模型。最后，設(shè)計(jì)了求解應(yīng)急物資配送問(wèn)題的改進(jìn)遺傳算法，提出一種改進(jìn)的比例選擇算子，既保證了種群的多樣性，又保證種群總是向著最優(yōu)解靠近；融入具有較強(qiáng)局部搜索能力的模擬退火算法，在一定程度上克服了基本遺傳算法的“早熟”收斂和易陷入局部最優(yōu)的缺陷；利用TSP問(wèn)題驗(yàn)證了改進(jìn)的遺傳算法的高效性；并以一次應(yīng)急物資配送實(shí)例，驗(yàn)證了配送模型的合理性和改進(jìn)的遺傳算法的有效性。
[Abstract]:In recent years, the frequent occurrence of natural disasters has brought great threat to the safety of people's life and property and the development of social economy. It is urgent to establish an efficient and rapid emergency rescue system. The timely and effective distribution of emergency materials is the most important part of the rescue work, and the accurate prediction of the demand for emergency materials is the prerequisite to ensure the smooth distribution. In this paper, the emergency material demand prediction and emergency material distribution are studied around the emergency rescue work. First of all, using indirect method to predict the demand of emergency materials in disaster area. BP neural network algorithm is a hot algorithm when predicting casualties in disaster area. In view of the disadvantage of low learning efficiency of standard BP neural network, it is easy to fall into local optimum. The method of combining additional momentum with adaptive adjusted learning rate is used to improve it, and a prediction model of casualties in disaster area based on BP neural network is established, and the number of casualties in 15 large earthquakes is predicted. The rationality of the prediction model and the validity of the algorithm are verified. Secondly, the tent demand in Wenchuan earthquake is estimated on the basis of the establishment of the emergency material demand prediction model. Secondly, based on the characteristics of emergency material distribution, a model of emergency material distribution with the shortest total delivery time and the premise of meeting the needs of disaster areas is constructed. Finally, an improved genetic algorithm is designed to solve the emergency material distribution problem, and an improved proportional selection operator is proposed, which not only ensures the diversity of the population, but also ensures that the population is always close to the optimal solution. The simulated annealing algorithm, which has strong local search ability, overcomes the "premature" convergence of the basic genetic algorithm and is easy to fall into the local optimum to some extent, and verifies the efficiency of the improved genetic algorithm by using the TSP problem. The rationality of the distribution model and the effectiveness of the improved genetic algorithm are verified by an emergency material distribution example.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：U116;F252

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 楊蕾;張苗苗;;時(shí)間序列模型在物流需求預(yù)測(cè)中的應(yīng)用[J];商業(yè)時(shí)代;2013年13期

相關(guān)博士學(xué)位論文 前1條

1 趙彤;我國(guó)突發(fā)自然災(zāi)害應(yīng)急救災(zāi)物資配送系統(tǒng)優(yōu)化研究[D];大連海事大學(xué);2011年

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