移动众包平台的任务酬金定价模型及调控策略研究——以珠三角地区为例
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引用本文:沈怡璇1,张 阳2.移动众包平台的任务酬金定价模型及调控策略研究——以珠三角地区为例[J].经济数学,2018,(4):80-84
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作者单位
沈怡璇1,张 阳2 (1.华中农业大学 公共管理学院湖北 武汉 4300702.华中科技大学 机械科学与工程学院湖北 武汉 430074) 
中文摘要:以珠三角地区移动众包平台为研究对象,实证分析了移动众包平台中的任务酬金定价模型和调控策略问题.首先使用GIS软件将移动众包平台中的用户和任务点的位置信息投射到真实地图,定性分析了影响任务点定价模型的交通、商圈等因素.然后建立以任务完成率最大和任务发布者收益最大的多目标任务酬金定价模型,使用熵权法确定模型中交通、商圈等因素的权值.最后借助贪心算法对上述模型进行求解,在分析部分任务未完成原因的基础上,提出了一种基于缓冲区打包的定价调控策略,通过实验分析,这种方法有助于提高任务的完成率和增加任务发布方的收益.
中文关键词:移动众包  定价模型  多目标规划  打包策略
 
The Tasks Reward Pricing Model of Mobile Crowdsourcing Platform and Research of Controling Strategyp——A Case Study of the Pearl River Delta Region
Abstract:Taking the mobile crowdsourcing platform of Pearl River Delta as an object, we conducted empirical analysis on pricing tasks reward and control strategy. First, using Arcgis,a kind of GIS software, and placing the geographic information of crowdsourcing platform users and tasks on real maps,so we can qualitatively analyze factors like transportation,commercial districts which influence tasks pricing model. Then a multi-objective pricing model was established which aimed to maximum tasks accomplishments rate and income of task publishers, and to use entropy weight method to get weights of factors such as transportation, commercial. Finally, with the help of greedy algorithm, we presented a pricing regulation strategy of package tasks based on buffer analysis on the basis of analyzing the reasons why some tasks are not completed. Through empirical analysis, we draw a conclusion that this method can improve tasks accomplishments rate and income of task publishers.
keywords:mobile crowdsourcing platform  pricing model  multi-objective programming  package strategy
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