本文關鍵詞： 代收貨款 EMS 物流配送 用戶行為分析 回歸分析 社區(qū)發(fā)現(xiàn) 信用分析　出處：《河北科技大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,代收貨款業(yè)務已經(jīng)成為EMS企業(yè)的主要業(yè)務增長點之一,代收貨款業(yè)務的特殊性使其在傳統(tǒng)的物流過程中附加了資金流。資金流的引入使得企業(yè)可以掌握大量的資金,但也加大了管理的難度。同時,各大物流企業(yè)代收貨款業(yè)務的競爭也越發(fā)激烈,各企業(yè)也分別從人員配置、系統(tǒng)安全、物流配送、人性化服務等方面改進代收貨款業(yè)務模式。本文以提升代收貨款業(yè)務同城物流配送成功率為目標,主要做了如下三個工作:(1)分析了代收貨款的產(chǎn)生背景、發(fā)展現(xiàn)狀、主要的業(yè)務模式以及當前的機遇和挑戰(zhàn)。確定以代收貨款業(yè)務中的用戶行為分析為出發(fā)點,改善其同城配送的服務質量。(2)從兩個方面對用戶行為進行了分析,其一是用戶信用分析,通過對大量交易記錄的整理,采用神經(jīng)網(wǎng)絡模型對代收貨款過程中的三類主要的參與者(攬投員,發(fā)貨方和收件方)建立信用評價體系;其二是用戶趨勢分析,針對發(fā)貨方的發(fā)貨量,通過關鍵點檢測方法發(fā)現(xiàn)用戶發(fā)貨量的時序數(shù)據(jù)中的關鍵點,分析關鍵點的成因以加深對業(yè)務規(guī)律的理解,通過線性回歸方法對用戶未來一段時間的發(fā)貨量進行一定程度的預測,為響應發(fā)貨變化提供依據(jù)。(3)以信用評級和趨勢預測為前提,本文構建了代收貨款的用戶網(wǎng)絡,并在此網(wǎng)絡上使用社區(qū)發(fā)現(xiàn)算法,挖掘針對攬投員的物流配送服務子網(wǎng)絡,通過引入個性化的配送體系,改善了代收貨款服務質量。結合具體案例,驗證了該方法的有效性。
[Abstract]:In recent years, the collection of loans on behalf of the business has become one of the main business growth points of EMS enterprises. The particularity of the collection of payment for goods makes it attach capital flow to the traditional logistics process. The introduction of capital flow enables the enterprise to master a large amount of funds, but also increases the difficulty of management. At the same time. The competition between the major logistics enterprises to collect the payment for goods is becoming more and more fierce, and the enterprises are also from the personnel configuration, system security, logistics distribution. This paper aims at improving the success rate of logistics distribution in the same city, mainly doing the following three work: 1) analyzing the background of the generation of the collection of goods. Development status, the main business models and current opportunities and challenges. To determine the collection of loans in the business of user behavior analysis as the starting point. Improve the service quality of distribution in the same city. 2) analyze the user behavior from two aspects, one is the user credit analysis, through a large number of transaction records collation. The neural network model is used to establish a credit evaluation system for the three main participants in the process of receiving payment for goods (bid takers, shippers and recipients). The second is the user trend analysis, through the key point detection method to find the key points in the timing data of the user shipments, and analyze the causes of the key points in order to deepen the understanding of the business law. Through the linear regression method to predict the future shipments of users to a certain extent, to provide a basis for responding to changes in the delivery of goods, based on credit rating and trend prediction as the premise. In this paper, we construct the user network of collecting payment, and use community discovery algorithm on this network to mine the sub-network of logistics distribution service for the bid takers, and introduce the personalized distribution system. The quality of service is improved. The effectiveness of this method is verified by a case study.
【學位授予單位】：河北科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F259.2

【參考文獻】

相關期刊論文 前8條

1 李戰(zhàn)江;;基于分層邏輯回歸的小企業(yè)信用評價模型[J];統(tǒng)計與決策;2016年07期

2 張瀾覺;趙昆;;基于BP神經(jīng)網(wǎng)絡的P2P信貸個人信用評價模型研究[J];電子技術與軟件工程;2015年12期

3 武志昊;林友芳;Steve Gregory;萬懷宇School of Computer and Information Technology,Beijing Jiaotong University;田盛豐;;Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks[J];Journal of Computer Science & Technology;2012年03期

4 牛秀明;;中國民營快遞業(yè)發(fā)展現(xiàn)狀與對策研究[J];物流技術(裝備版);2011年24期

5 姜珊珊;;中國郵政快遞(EMS)發(fā)展現(xiàn)狀與對策研究[J];現(xiàn)代商貿(mào)工業(yè);2011年23期

6 王公強;;物流企業(yè)代收貨款的風險及管理措施[J];經(jīng)濟導刊;2010年12期

7 王娜;;淺析快遞公司對代收貨款業(yè)務的監(jiān)管問題[J];市場周刊(理論研究);2010年06期

8 施錫銓,鄒新月;典型判別分析在企業(yè)信用風險評估中的應用[J];財經(jīng)研究;2001年10期

相關博士學位論文 前1條

1 楊聚平;以客戶為中心“最后一公里”配送模式研究[D];對外經(jīng)濟貿(mào)易大學;2014年

相關碩士學位論文 前3條

1 廖利軍;武漢郵政EMS同城配送網(wǎng)絡組織與優(yōu)化的應用研究[D];華中科技大學;2013年

2 張泳;面向物流企業(yè)的墊付—代收貸款業(yè)務模式研究[D];杭州電子科技大學;2013年

3 俞洋洋;云計算背景下第三方物流企業(yè)代收貨款模式研究[D];重慶交通大學;2012年

，

本文編號：1462961