本文選題：快遞業(yè)務(wù)量 + 預(yù)測(cè)��； 參考：《浙江工商大學(xué)》2015年碩士論文

【摘要】：快遞業(yè)是我國(guó)的新興行業(yè),發(fā)展迅速,2010年至2013年我國(guó)快遞業(yè)務(wù)量從23.4億件上升到92億件,年增長(zhǎng)率高達(dá)40%；2014年全年,我國(guó)快遞服務(wù)企業(yè)業(yè)務(wù)量累計(jì)完成139.6億件,躍居世界第一,業(yè)務(wù)收入累計(jì)完成2045.4億元,同比增長(zhǎng)41.9%。盡管快遞業(yè)發(fā)展速度迅猛,但其發(fā)展仍然存在諸多問(wèn)題,例如：經(jīng)營(yíng)成本高、經(jīng)濟(jì)效益較低、服務(wù)質(zhì)量不高、顧客滿(mǎn)意度低等。尤其在快遞業(yè)務(wù)量快速增長(zhǎng)的背景下,這勢(shì)必會(huì)影響快遞業(yè)長(zhǎng)遠(yuǎn)發(fā)展。為此,本文從宏觀的快遞業(yè)務(wù)量預(yù)測(cè)和微觀的客戶(hù)點(diǎn)分析這兩個(gè)方面來(lái)分析如何提高服務(wù)質(zhì)量和顧客滿(mǎn)意度。本文首先構(gòu)建了基于影響因素的快遞業(yè)務(wù)量關(guān)系模型和基于時(shí)間序列的區(qū)域快遞業(yè)務(wù)量組合預(yù)測(cè)模型,對(duì)區(qū)域快遞業(yè)務(wù)量進(jìn)行預(yù)測(cè)。根據(jù)基于影響因素的快遞業(yè)務(wù)量關(guān)系模型得出市轄區(qū)2005-2014年的快遞業(yè)務(wù)量,再根據(jù)基于時(shí)間序列的區(qū)域快遞業(yè)務(wù)量組合預(yù)測(cè)模型得出區(qū)域未來(lái)三年的快遞業(yè)務(wù)量；其次,根據(jù)區(qū)域的快遞量預(yù)測(cè)結(jié)果,明確該區(qū)在未來(lái)具體需要的配送車(chē)輛數(shù)以及每輛車(chē)的具體接駁點(diǎn)位置。針對(duì)配送車(chē)輛數(shù)問(wèn)題,先通過(guò)調(diào)研獲取1輛車(chē)每年的配送快遞量,然后根據(jù)區(qū)域快遞預(yù)測(cè)量,求出配送所需的車(chē)輛數(shù)。針對(duì)每輛車(chē)的具體接駁點(diǎn)地址問(wèn)題,構(gòu)建選址模型對(duì)快遞接駁點(diǎn)進(jìn)行選址。模型包括三部分：第一,利用改進(jìn)的K-means算法對(duì)客戶(hù)點(diǎn)地址數(shù)據(jù)進(jìn)行聚類(lèi)分析；第二,對(duì)聚類(lèi)得到的每類(lèi)數(shù)據(jù)利用重心法求得初選接駁點(diǎn)位置；第三,通過(guò)頻度分析以及停車(chē)難易度分析確定最優(yōu)接駁點(diǎn)位置。最后,通過(guò)實(shí)證分析進(jìn)行驗(yàn)證：第一,利用所建模型得出杭州市上城區(qū)未來(lái)三年的快遞業(yè)務(wù)量；第二,利用得出的快遞業(yè)務(wù)量數(shù)據(jù)以及某快遞公司提供的其在杭州市某區(qū)域某時(shí)間周期內(nèi)的真實(shí)數(shù)據(jù),通過(guò)接駁點(diǎn)選址模型確定該區(qū)域最優(yōu)的接駁點(diǎn)位置。
[Abstract]:Express delivery industry is a new industry in China, which has developed rapidly. From 2010 to 2013, the volume of express delivery business in our country rose from 2.34 billion to 9.2 billion, with an annual growth rate of 40%. In 2014, the total business volume of express delivery service enterprises in China reached 13.96 billion pieces, ranking first in the world. Total business income completed 204.54 billion yuan, an increase of 41.9%. Despite the rapid development of express industry, there are still many problems in its development, such as high operating cost, low economic benefit, low service quality, low customer satisfaction and so on. Especially in the context of rapid growth of express delivery business, this will inevitably affect the long-term development of the express industry. Therefore, this paper analyzes how to improve the quality of service and customer satisfaction from two aspects of macro express delivery volume prediction and micro customer point analysis. In this paper, first of all, the relationship model of express service volume based on influence factors and the combined forecasting model of regional express business volume based on time series are constructed to predict the regional express business volume. According to the relationship model of express delivery volume based on influencing factors, the express business volume from 2005 to 2014 is obtained, and then the regional express business volume in the next three years is obtained according to the forecasting model of regional express business volume based on time series. Secondly, According to the forecast result of regional express quantity, the number of distribution vehicles needed in the future and the location of the specific connection point of each vehicle in the area are determined. In order to solve the problem of the number of distribution vehicles, we first obtain the annual delivery quantity of one vehicle through investigation and research, and then calculate the number of vehicles needed for distribution according to the regional express forecast. To solve the problem of the specific connection point address of each vehicle, the location model is constructed to locate the express connection point. The model consists of three parts: first, the improved K-means algorithm is used to cluster the customer point address data; second, the center of gravity method is used to obtain the location of the primary connection point; third, Through frequency analysis and parking difficulty analysis to determine the optimal location of the connection point. Finally, through the empirical analysis to verify: first, using the established model to get the next three years of Hangzhou Shangcheng express business volume; second, By using the data of express delivery volume and the real data provided by a express delivery company in a certain period of time in a certain area of Hangzhou, the optimal location of the connection point in this area is determined by the location model of the connection point.
【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】：2015
【分類(lèi)號(hào)】：F259.2

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本文編號(hào)：1858968