Font Size: a A A

Design And Implement Of Logistics Credit Evaluation System For E-Commerce

Posted on:2012-05-29Degree:MasterType:Thesis
Country:ChinaCandidate:X L ZhangFull Text:PDF
GTID:2189330338984232Subject:Software engineering
Abstract/Summary:PDF Full Text Request
With the rapid development of E-commerce, the importance of logistics has become increasingly evident. To complete the space transfer of goods timely and accurately is the key to success of E-commerce. Because of the limitation of domestic logistics level and lack of relevant standards and such factors, long distribution time, lost or damaged goods and poor quality of logistics services are common occurrence, making logistics disputes as the main disputes in E-commerce. Most of E-commerce enterprises are facing the problems that have to choose logistics partner without grasping the credit of logistics enterprises and logistics persons, affected their choices. And the demand to know the basic information and credit information of logistics enterprises and logistics person are growing, but there are no such complete logistics credit evaluation system for E-commerce at present.In this paper, with in-depth research and analysis on existing credit evaluation system and credit evaluation models, considering the characteristics of E-commerce logistics and the logistics credit requirement of E-commerce enterprises, we propose the logistics credit evaluation system for E-commerce. It is composed of credit evaluation index system, credit scoring models and credit evaluation methods of logistics. The credit index system of logistics is designed using Delphi method according the principle of index design. From the characteristics of E-commerce logistics, the index system is divided into logistics enterprise credit evaluation index system and logistics person credit evaluation index system. The improved credit scoring models not only consider the given score but also the credit of rater, transaction amount, transaction times, evaluation time and other factors. And the validity of improved credit scoring models is proved by experiments. With in-depth research on clustering algorithm and Fuzzy Support Vector Machin(eFSVM) and also the requirement of logistics credit evaluation system for E-commerce about accuracy and time performance, we realized Fussy Multi-class Support Vector Machine (FMSVM) based on Clustering, in which K-WMeans clustering algorithm and hierarchical clustering algorithm based on similarity is introduced to FMSVM in order to improve the performance of proposed algorithm, and the experimental results proves this. Also credit degrees are designed. With the needs of logistics credit evaluation system for E-commerce, system architecture is divided into C/S subsystem, database layer and B/S subsystem. C/S subsystem is mainly used for management of basic information and credit information about logistics enterprises and logistics persons. Database layer is used to store related basic information and credit information. And B/S subsystem is mainly used to accept E-commerce logistics information input by E-commerce enterprises , search and shows logistics credit information of logistics enterprises and logistics persons.The paper firstly summarizes the existing technology of credit evaluation, analyzes and compares the achieved results of domestic and foreign scholars and enterprises. Then sum up the functional requirements of logistics credit evaluation system for E-commerce and existing problems. After that, combined with the characteristics of E-commerce logistics, design credit evaluation index system of logistics, improve the existing credit scoring models and propose fuzzy multi-class support vector machine based on clustering to improve the reliability of the results of credit evaluation and time performance, and also credit degrees are designed. Finally, this paper describes the architecture and implementation of logistics credit evaluation system for E-commerce, and then presents the system.
Keywords/Search Tags:E-commerce, Logistics, Credit evaluation, Clustering, Fussy multi-class support vector machine
PDF Full Text Request
Related items