空间众包环境下的任务定价模型研究

在综合考虑平台、商家和会员三方相互作用的基础上,通过建立包含任务动态分配机制的动态规划模型,结合金融定价思想刻画任务定价问题,并通过空间可视化对珠三角地区劳务众包平台数据进行实证研究.为提高模型的实用性,利用K-means聚类分析对任务打包并引入激励规则对动态定价模型进行了改进.最后,通过模拟仿真得出改进后模型的任务完成率为88.10%,相比平台现有定价模型(62.50%)和改进前的动态定价模型(85.20%)任务完成情况有较大幅度的提升.为基于地理位置的服务平台的商品定价、以及地理位置信息与平台会员的关系等实证和应用研究提供了理论与实践参考.

Study on Task Pricing Model in Space Crowdsourcing Environment: A Case Study of the Pearl River Delta Region

Through conducting spatial visualization on the data from Crowdsourcing platform of the PRD region and applying financial pricing philosophy into Crowdsourcing mission scenarios, this paper established a dynamic programming pricing model with task automatic allocation scheme after comprehensive consideration among the task releasing entrepreneur, the receiver and the Crowdsourcing platform. In order to hone the model for actual application, the mission packing by K-means clustering and motivation rules improved on the dynamic programming pricing model. After solving the model and analog simulation, the latest dynamic model proved its effectiveness at the mission completion rate of 88.10%, while the rate of the present model at the platform was 62.50% and the pricing model before improvement was 85.20%. In conclusion, it provides much mentality for the location based service (LBS) platforms like express errands service, DiDi and Meituan takeout on quantifying the relation among service pricing, space location and members for both empirical study and application in reality.