精英蜂群算法及考虑利益相关者的众包定价模型
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摘要
从众包活动参与者即主体企业、众包平台、平台会员的视角出发,研究服务众包定价问题。在考虑会员信誉度和任务聚集度的基础上,针对含有地理因素的众包任务设计打包分配定价方案。以服务成本、任务价值、会员收益等为导向,对不同任务进行组合配置,从而设计多目标规划任务配置及定价模型,并针对该模型构建了精英蜂群算法。在精英蜂群算法中,充分利用蜜源信息并着重考虑成长性较好的蜜蜂,进而避免了局部最优问题,提高了搜索效率。通过对众包服务企业运营数据进行分析,获取到众包服务会员特征及任务完成相关基础信息,以此进行仿真实验。仿真结果表明,通过众包任务打包定价机制,任务完成率、企业总成本、三方总收益等方面均有显著优化。综合模型及数据实验可知,众包任务在定价与发布过程中根据自身特征差异,在无差异服务中只需要考虑会员信誉度,对于具有服务差异性的任务则需要考虑打包发布。
This paper studied the pricing problem of crowdsourcing model from the perspective of participants,which included enterprises,crowdsourcing platforms and members. On the basis of considering membership credibility and task aggregation degree,it designed an allocation pricing scheme for crowdsourcing tasks with geographical factor. Guided by service cost,task value,member income,it constructed a multi objective programming model by combining different tasks,and designed the elitism bee colony algorithm for the model. In this algorithm,through the use of nectar source information and considering the improvement of bees,it significantly improved the search efficiency. Then analyzing E-commerce enterprise operation data,this paper got crowdsourcing membership characteristics,task completion rate and other basic information,and carried out the simulation experiment. Simulation results show that the task packaging pricing strategy has significant advantages in task completion rate,enterprise total cost,and the three parts total revenue. Through the model and data experiments,it can be seen that crowdsourcing has its own characteristics in pricing and publishing. For nondifferentiated services,it only needs to consider the reputation of members,for differentiated services,the task assignment needs to be packaged.
This paper studied the pricing problem of crowdsourcing model from the perspective of participants,which included enterprises,crowdsourcing platforms and members. On the basis of considering membership credibility and task aggregation degree,it designed an allocation pricing scheme for crowdsourcing tasks with geographical factor. Guided by service cost,task value,member income,it constructed a multi objective programming model by combining different tasks,and designed the elitism bee colony algorithm for the model. In this algorithm,through the use of nectar source information and considering the improvement of bees,it significantly improved the search efficiency. Then analyzing E-commerce enterprise operation data,this paper got crowdsourcing membership characteristics,task completion rate and other basic information,and carried out the simulation experiment. Simulation results show that the task packaging pricing strategy has significant advantages in task completion rate,enterprise total cost,and the three parts total revenue. Through the model and data experiments,it can be seen that crowdsourcing has its own characteristics in pricing and publishing. For nondifferentiated services,it only needs to consider the reputation of members,for differentiated services,the task assignment needs to be packaged.
引文
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[9]He Zhengwen,Wang Nengmin,Li Pengxiang.Simulated annealing for financing cost distribution based project payment scheduling from a joint perspective[J].Annals of Operations Research,2014,213(1):203-220.
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[11]邱剑锋,谢娟,汪继文.基于交叉突变算子的人工蜂群算法及其应用[J].计算机应用研究,2014,31(5):1336-1341.(Qiu Jianfeng,Xie Juan,Wang Jiwen.Improved artificial bee colony based on intersect mutation operator and its application[J].Application Research of Computers,2014,31(5):1336-1341.)
[12]周清雷,陈明昭,张兵.多目标人工蜂群算法在服务组合优化中的应用[J].计算机应用研究,2012,29(10):3625-3628.(Zhou Qinglei,Chen Mingzhao,Zhang Bing.Multi-objective artificial bee colony algorithm applied in Qo S-aware service composition optimization[J].Application Research of Computers,2012,29(10):3625-3628.)
[13]刘巧莉,温浩宇,Hong Qin.P2P网络信贷中投资行为影响因素研究---基于拍拍贷平台交易的证据[J].管理评论,2017,29(6):13-22,52.(Liu Qiaoli,Wen Haoyu,Hong Qin.Determinants of lending behaviors in online P2P lending market:based on the lending data of PPDai.com[J].Management Review,2017,29(6):13-22,52.)
[14]Marsden P V.Network data and measurement[J].Annual Review of Sociology,1990,16:435-463.
[15]Howe J.The rise of crowdsourcing[J].Jenkins H Convergence Culture Where Old&New Media Collide,2006,14(6):1-5.
[2]Mason W,Watts D J.Financial incentives and the“performance of crowds”[J].ACM SIGKDD Explorations Newsletter,2010,11(2):100-108.
[3]付磊,廖成林.考虑消费者类型的零售商物流外包竞争策略[J].计算机集成制造系统,2017,23(1):134-143.(Fu Lei,Liao Chenglin.Retailer logistics outsourcing and competition of TPLs considering the consumer demand characteristics[J].Computer Integrated Manufacturing Systems,2017,23(1):134-143.)
[4]彭彬,赵征.基于两阶段动态博弈的服务外包定价分析[J].运筹与管理,2012,21(3):154-158.(Peng Bin,Zhao Zheng.Service outsourcing pricing based on two-stage dynamic game[J].Operations Research and Management Science,2012,21(3):154-158.)
[5]陆晓菁,陈宏民.电子口碑虚拟社区平台定价策略[J].上海交通大学学报,2014,48(2):300-305.(Lu Xiaojing,Chen Hongmin.Pricing strategies of e WOM virtual communities[J].Journal of Shanghai Jiaotong University,2014,48(2):300-305.)
[6]吴春旭,黄一络,许传永.正负网络效应共存的社交平台定价问题[J].上海交通大学学报,2015,49(4):519-523.(Wu Chunxu,Huang Yiluo,Xu Chuanyong.Pricing problem of social network platforms with co-existence of positive and negative network effects[J].Journal of Shanghai Jiaotong University,2015,49(4):519-523.)
[7]Zhang Yu,Van Der Schaar M.Reputation-based incentive protocols in crowdsourcing applications[C]//Proc of IEEE INFOCOM.Piscataway,NJ:IEEE Press,2012:2140-2148.
[8]郝琳娜,侯文华,张李浩,等.基于众包虚拟社区的诚信保障和信誉评价机制研究[J].系统工程理论与实践,2014,34(11):2837-2848.(Hao Linna,Hou Wenhua,Zhang Lihao,et al.Research on integrity safeguard mechanism and reputation evaluation mechanism of crowdsourcing in the virtual community[J].System Engineering Theory and Practice,2014,34(11):2837-2848.)
[9]He Zhengwen,Wang Nengmin,Li Pengxiang.Simulated annealing for financing cost distribution based project payment scheduling from a joint perspective[J].Annals of Operations Research,2014,213(1):203-220.
[10]Kong Dequan,Tiong R L,Cheah C Y,et al.Assessment of credit risk in project finance[J].Journal of Construction Engineering&Management,2008,134(11):876-884.
[11]邱剑锋,谢娟,汪继文.基于交叉突变算子的人工蜂群算法及其应用[J].计算机应用研究,2014,31(5):1336-1341.(Qiu Jianfeng,Xie Juan,Wang Jiwen.Improved artificial bee colony based on intersect mutation operator and its application[J].Application Research of Computers,2014,31(5):1336-1341.)
[12]周清雷,陈明昭,张兵.多目标人工蜂群算法在服务组合优化中的应用[J].计算机应用研究,2012,29(10):3625-3628.(Zhou Qinglei,Chen Mingzhao,Zhang Bing.Multi-objective artificial bee colony algorithm applied in Qo S-aware service composition optimization[J].Application Research of Computers,2012,29(10):3625-3628.)
[13]刘巧莉,温浩宇,Hong Qin.P2P网络信贷中投资行为影响因素研究---基于拍拍贷平台交易的证据[J].管理评论,2017,29(6):13-22,52.(Liu Qiaoli,Wen Haoyu,Hong Qin.Determinants of lending behaviors in online P2P lending market:based on the lending data of PPDai.com[J].Management Review,2017,29(6):13-22,52.)
[14]Marsden P V.Network data and measurement[J].Annual Review of Sociology,1990,16:435-463.
[15]Howe J.The rise of crowdsourcing[J].Jenkins H Convergence Culture Where Old&New Media Collide,2006,14(6):1-5.