Research On Service Matching And Optimization In Crowd-sourcing Delivery

In the era of mobile Internet,e-commerce logistics and the development of various 020 services have increased rapidly.The 'last three kilometers' of logistics distribution has become the key area of the giants in their respective fields.With the introduction and popularization of the shared economic concept,crowd-sourcing delivery is introduced as a new model.The core of the model based on the concept of crowd-sourcing is to effectively integrate and utilize social idle resources,make the fragmented distribution resources used for the logistics distribution information platform or enterprise,and reduce the cost of logistics and organization.However,there are still many problems in the supply and demand matching mechanism and user experience of crowd-sourcing distribution.First of all,the service content,business scale and operation mode of the delivery platform are compared among the typical crowd-sourcing delivery platform,business process analysis of the F-platform and the supply and demand analysis of the delivery service-the service content,the business scale and the operation mode of the distribution platform,such as the Meituan crowd-sourcing,the Eleme Fengniao crowd-sourcing and so on.It is found that the mode of crowdsourcing delivery presents a growth trend in terms of the number of cities that covered,daily orders and Delivery staff.This paper reviews the process of pick-up,distribution and delivery under the control of the open crowd-sourcing platform,summarizes the different services of the F-platform for the terminal delivery,and points out the inaccuracy of service matching in the comparison of the self-operating mode and the poor quality of service.Through analysis of code of F-platform and reconstruction of fishbone figure for users,satisfaction,this paper puts forward the influencing factors of delivery:location information,delivery time window,task status and quality of service.In addition,this paper further examines the rationality of the impact factors by the method of questionnaire and demonstrates the problems need to be solved.Then,to solving the problem step by step,this paper proposes to optimize the matching mechanism and service quality by establishing the semantic Web matching mechanism and service optimization model with Grey Relational Analysis respectively.The first step is to divide various influence factors by functional and non functional attributes,and build the ontology model of crowd-sourcing delivery service.In this paper,the original algorithm of geometric distance and information amount are improved from the concept and the business logic in the domain of delivery.The results show that the improved algorithm achieves a better balance between rate of recall and precision than the original algorithm.The second step is to apply the improved algorithm to match the users' orders and services.Considering that the quality of service quality can't be quantified,we collects the data of crowd-sourcing delivery staff and use them to the calculation of Grey Relational Analysis and AHP to optimize the service.Finally,on the basis of feasibility analyzing,we choose the method of modular to design active matching and service optimization,and apply it to the crowdsourcing delivery platform.Then,the conceptualization of service publishing and registration,active service matching and service optimization is implemented.From active matching to quality optimization,it provides a new idea for optimizing the crowd-sourcing delivery service with information asymmetry and poor service quality in two stages.