[1]赖朝安,侯延行.基于双层随机规划的云监控平台定价策略[J].深圳大学学报理工版,2020,37(4):433-440.[doi:10.3724/SP.J.1249.2020.04432]
 LAI Chaoan and HOU Yanhang.Pricing strategy of cloud monitoring platform based on bilevel stochastic programming[J].Journal of Shenzhen University Science and Engineering,2020,37(4):433-440.[doi:10.3724/SP.J.1249.2020.04432]
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基于双层随机规划的云监控平台定价策略()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第37卷
期数:
2020年第4期
页码:
433-440
栏目:
电子与信息科学
出版日期:
2020-07-15

文章信息/Info

Title:
Pricing strategy of cloud monitoring platform based on bilevel stochastic programming
文章编号:
202004014
作者:
赖朝安侯延行
华南理工大学工商管理学院,广东广州 510640
Author(s):
LAI Chaoan and HOU Yanhang
School of Business Administration, South China University of Technology, Guangzhou 510640, Guangdong Province, P.R.China
关键词:
工业工程定价策略云服务云监控双层规划不确定性动态博弈利润最大化极点搜索
Keywords:
industrial engineering pricing strategy coloud service cloud monitoring bilevel programming uncertainty dynamic game profit moximization pole search
分类号:
F224.1; C931.1
DOI:
10.3724/SP.J.1249.2020.04432
文献标志码:
A
摘要:
定价是企业运营的关键决策,云服务作为新兴产业,目前还未形成统一的定价标准.针对云监控服务平台的定价问题,分析云监控服务提供商与客户之间的利益关系,建立两阶段动态博弈模型,围绕利润最大化目标提取出核心参数与变量,同时考虑设备故障与生产损失等方面的不确定性,构建出双层随机规划模型,结合极点搜索思想给出相应的求解算法,并通过实例证明模型的有效性.计算结果显示,提供商利润随着客户数量的增加呈递增趋势,同时多类型的服务模式在一定程度上有助于服务提供商利润的增加;调节定价策略,让较大客户购买基础服务类型,剩余客户选择深层服务,往往可使得服务提供商的利润达到最优.
Abstract:
Pricing is a key decision of enterprise operation. As a new industry, the cloud service has not yet formed a unified pricing standard. Aiming at the pricing problem of cloud monitoring service platform, this paper analyzes the interest relationship between providers of cloud monitoring service and customers, establishes a two-stage dynamic game model, and extracts the core parameters and variables for the target of profit maximization. This paper also takes into account the uncertainty of equipment failure and production loss, and constructs a bilevel stochastic programming model, then a new algorithm combined with pole search method is proposed to find the global optinal solution. Finally, an example is given to demonstrate the model effectiveness. The computation results show that the profit of the provider is increasing with the increase of the number of customers. At the same time, the multi-type service mode will help the service provider increase its profit to a certain extent. By adjusting the pricing strategy, the platform makes most customers purchase the basic service type and the remaining customers choose the deep service, often optimizes the profits of service providers.

参考文献/References:

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备注/Memo

备注/Memo:
Received:2019-06-16;Accepted:2019-10-22
Foundation:Natural Science Foundation of Guangdong Province (2018A030313079); Soft Science Project of Guangdong Province (2019A101002006); The 13th Five-Year Plan for the Development of Philosophy and Social Sciences in Guangzhou (2018GZYB16)
Corresponding author:Associate professor LAI Chaoan. E-mail: chalai@scut.edu.cn
Citation:LAI Chaoan, HOU Yanhang. Pricing strategy of cloud monitoring platform based on bilevel stochastic programming[J]. Journal of Shenzhen University Science and Engineering, 2020, 37(4): 433-440.(in Chinese)
基金项目:广东省自然科学基金资助项目(2018A030313079);广东省软科学资助项目(2019A101002006);广州市哲学社会科学发展“十三五”规划资助项目(2018GZYB16)
作者简介:赖朝安(1973—),华南理工大学副教授、博士.研究方向:智能制造和技术预见.E-mail: chalai@scut.edu.cn
引文:赖朝安,侯延行.基于双层随机规划的云监控平台定价策略[J]. 深圳大学学报理工版,2020,37(4):433-440.
更新日期/Last Update: 2020-07-26