一种新型劳务众包平台的市场策略与定价研究
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引用本文:彭浦源,李迎新,黄 正,王颖喆.一种新型劳务众包平台的市场策略与定价研究[J].经济数学,2018,(3):41-46
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彭浦源,李迎新,黄 正,王颖喆 (北京师范大学 数学科学学院 北京 海淀 100875) 
中文摘要:定义并研究了自助式空间劳务众包这一新型众包模式,给出相关定义和众包平台的运行规则,提出会员行为仿真算法模拟市场.根据这类劳务众包平台增加利润和扩大市场份额的普遍需求,基于双边市场理论,提出任务打包机制、定价多目标规划模型和抢单顺序多目标规划模型.采用带精英策略的非支配排序的遗传算法(NSGA-Ⅱ)求解问题的帕累托解集(Pareto Front),最后应用逼近理想点法(TOPSIS)根据平台战略目标给出最优解.规划模型在数据集上应用效果良好,任务完成率能在有效控制成本的前提下提升30%至45%,新用户参与率能在保证较高的任务完成率的前提下提升150%至250%.
中文关键词:应用数学  众包任务定价模型  多目标优化  NSGA-Ⅱ
 
Marketing Strategies and a Pricing Model for a New Crowdsourcing Platform
Abstract:A new model of crowdsourcing: the self-service spatial crowd sourcing (SC) has been defined. Important definitions and the operating rules of SC platforms are introduced. Then, based on the requirements of SC platforms which is increasing profit as well as expanding market share, and enlightened by bilateral market theory, a tasks-packing system, multi-objective models for tasks pricing and participation ordering are framed. For the computational solution, an improved Genetic Algorithm Ⅱ (NSGA-Ⅱ) to search for Pareto-optimal solutions has been developed, and then determine the best solution by using the ideal-point method (TOPSIS). The weights of objectives are ascertained based on the preference of decision makers.Models and algorithms are conducted on the dataset from CUMCM 2017. The results show that the task completion rate can increase by 30% to 45% while the total cost does not increase significantly, and participation rate of new users can increase by 150% to 250% while task completion remains at a high level.
keywords:applied mathematics  crowdsourcing tasks pricing model  multi-objective optimization  NSGA-Ⅱ
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