本文選題：公路貨運(yùn)　切入點(diǎn)：運(yùn)價(jià)制定　出處：《浙江工商大學(xué)》2017年碩士論文

【摘要】：公路貨運(yùn)是指在公路上,主要運(yùn)輸工具為貨車(chē)的一種運(yùn)輸方式,是交通運(yùn)輸系統(tǒng)的重要組成部分。截止到2015年,我國(guó)高速公路總里程數(shù)已達(dá)457.73萬(wàn)公里,居世界第一,僅2015年公路貨運(yùn)量達(dá)315億噸,占總貨運(yùn)量的76.8%。"無(wú)車(chē)承運(yùn)人"是公路貨運(yùn)的一種新型商業(yè)模式,是指無(wú)自有車(chē)輛的公司或單位,進(jìn)行承接物流運(yùn)輸項(xiàng)目,隨著"互聯(lián)網(wǎng)+物流"模式的逐漸推廣,大量的傳統(tǒng)貨運(yùn)企業(yè)向"無(wú)車(chē)承運(yùn)人"企業(yè)轉(zhuǎn)型,為了提高新型貨運(yùn)企業(yè)與實(shí)際承運(yùn)商之間合作效率,在雙方協(xié)定運(yùn)價(jià)時(shí),提供一個(gè)合理的參考運(yùn)價(jià)具有重大意義。本文以公路貨運(yùn)的運(yùn)價(jià)制定問(wèn)題為研究對(duì)象,使用多元線性回歸模型、BP神經(jīng)網(wǎng)絡(luò)模型和基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)模型(簡(jiǎn)稱(chēng)為GA-BP模型)進(jìn)行研究。主要研究工作及成果總結(jié)如下:(1)綜述了公路貨運(yùn)行業(yè)現(xiàn)狀,提出了公路貨運(yùn)運(yùn)價(jià)制定問(wèn)題,及其研究的價(jià)值與意義,綜述了該問(wèn)題研究現(xiàn)狀;介紹了相關(guān)啟發(fā)式算法,并進(jìn)行比較。(2)介紹了 "無(wú)車(chē)承運(yùn)人"概念的由來(lái),及其運(yùn)作模式;針對(duì)公路貨運(yùn)運(yùn)價(jià)制定問(wèn)題,結(jié)合對(duì)無(wú)車(chē)承運(yùn)人企業(yè)的調(diào)研,對(duì)影響運(yùn)價(jià)的因素進(jìn)行分析選取,并進(jìn)行灰色關(guān)聯(lián)度分析,最終選取了國(guó)家經(jīng)濟(jì)發(fā)展水平、燃油價(jià)格、市場(chǎng)需求量、貨物重量、路程,并在此基礎(chǔ)上建立了多元線性回歸模型。(3)由于BP神經(jīng)網(wǎng)絡(luò)對(duì)非線性關(guān)系的有很強(qiáng)的適用性,因此將BP神經(jīng)網(wǎng)絡(luò)算法應(yīng)用于公路貨運(yùn)運(yùn)價(jià)制定問(wèn)題,增加考慮非線性影響因素車(chē)型和承運(yùn)商,構(gòu)建7-15-1型的神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)。(4)針對(duì)BP神經(jīng)網(wǎng)絡(luò)的收斂速度慢缺點(diǎn),本文使用LM算法優(yōu)化;針對(duì)BP神經(jīng)網(wǎng)絡(luò)算法的易陷入局部最優(yōu)解的缺點(diǎn),本文將遺傳算法與BP網(wǎng)絡(luò)算法結(jié)合,建立基于GA-BP算法的運(yùn)價(jià)制定模型,采用遺傳算法優(yōu)化BP網(wǎng)絡(luò)的初始權(quán)值與閾值,極大提高了算法搜索全局最優(yōu)解的能力。(5)研究了公路貨運(yùn)模型的應(yīng)用。根據(jù)M貨運(yùn)公司提供的實(shí)際經(jīng)營(yíng)數(shù)據(jù),分別對(duì)多元線性回歸模型、BP神經(jīng)網(wǎng)絡(luò)模型、GA-BP模型進(jìn)行應(yīng)用研究。通過(guò)實(shí)例分析驗(yàn)證了本文提出的基于GA-BP算法運(yùn)價(jià)制定模型的有效性與實(shí)用價(jià)值。
[Abstract]:Highway freight is a kind of transportation mode in highway, where the main means of transport is a freight car. It is an important part of the transportation system. By 2015, the total mileage of expressway in China has reached 4.5773 million kilometers, ranking first in the world. In 2015 alone, the volume of goods transported by road amounted to 31.5 billion tons, accounting for 76.88 percent of the total cargo volume. "Carrier without vehicles" is a new business model for road freight transport, which refers to companies or units that do not have their own vehicles to undertake logistics and transportation projects. With the gradual promotion of the mode of "Internet logistics", a large number of traditional freight enterprises have been transformed to "carless carriers". In order to improve the efficiency of cooperation between new freight enterprises and actual carriers, when the two parties agree on freight rates, It is of great significance to provide a reasonable reference rate. Using multiple linear regression model and BP neural network model based on genetic algorithm optimization BP neural network model (referred to as GA-BP model). The main research work and results are summarized as follows: 1) the current situation of road freight industry is summarized. This paper puts forward the problem of highway freight freight pricing, and the value and significance of the research, summarizes the current research situation of the problem, introduces the relevant heuristic algorithms, and compares the concept of "car-free carrier", and introduces the origin and operation mode of the concept of "car-free carrier". In view of the problem of road freight freight tariff formulation, combined with the investigation of non-vehicle carrier enterprises, the factors affecting freight rate are analyzed and selected, and the grey correlation degree analysis is carried out. Finally, the national economic development level and fuel price are selected. Market demand, cargo weight, distance, and on this basis, a multivariate linear regression model is established. Therefore, the BP neural network algorithm is applied to the pricing problem of road freight transportation, and considering the nonlinear influence factors of vehicle type and carrier, the neural network structure of 7-15-1 type is constructed, which aims at the slow convergence speed of BP neural network. In this paper, LM algorithm is used to optimize, and the BP neural network algorithm is easy to fall into the local optimal solution. In this paper, the genetic algorithm and BP network algorithm are combined to establish the pricing model based on GA-BP algorithm. Genetic algorithm is used to optimize the initial weight and threshold of BP network, which greatly improves the ability of searching global optimal solution.) the application of road freight model is studied. According to the actual operating data provided by M freight company, The application of GA-BP model, a multivariate linear regression model, is studied, and the validity and practical value of the model based on GA-BP algorithm are verified by an example.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F542.5;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 王燕凌;;中國(guó)公路貨運(yùn)市場(chǎng)供求分析[J];物流技術(shù)與應(yīng)用(貨運(yùn)車(chē)輛);2012年07期

2 董娜;;無(wú)車(chē)承運(yùn)人的優(yōu)勢(shì)分析和發(fā)展建議[J];交通標(biāo)準(zhǔn)化;2011年24期

3 陳艷靜;;公路貨運(yùn)價(jià)格形成機(jī)制影響因素分析與對(duì)策研究[J];商業(yè)文化(下半月);2011年08期

4 馬銀波;;我國(guó)公路貨運(yùn)價(jià)格傳導(dǎo)機(jī)制研究[J];價(jià)格理論與實(shí)踐;2009年02期

5 馬銀波;;公路貨運(yùn)價(jià)格與需求動(dòng)態(tài)關(guān)系的實(shí)證分析[J];長(zhǎng)安大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2008年03期

6 康自平,杜偉;我國(guó)民航運(yùn)價(jià)的管制放松與再管制[J];財(cái)經(jīng)問(wèn)題研究;2004年12期

7 葛少云,劉自發(fā),余貽鑫;基于改進(jìn)禁忌搜索的配電網(wǎng)重構(gòu)[J];電網(wǎng)技術(shù);2004年23期

，

本文編號(hào)：1684515