Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory

doi:10.21629/JSEE.2018.02.13

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (2): 321-335.doi: 10.21629/JSEE.2018.02.13

• Systems Engineering • Previous Articles Next Articles

Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory

Chao GENG¹(), Shiyou QU¹(), Yingying XIAO^2,^3,^4,*(), Mei WANG^2,³(), Guoqiang SHI^2,^3,⁴(), Tingyu LIN^2,^3,⁴(), Junjie XUE²(), Zhengxuan JIA²()

¹ Economics and Management School, Harbin Institute of Technology at Weihai, Weihai 264209, China
² Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, China
³ State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China
⁴ Science and Technology on Space System Simulation Laboratory, Beijing Simulation Center, Beijing 100854, China

Received:2017-04-27 Online:2018-04-26 Published:2018-04-27
Contact: Yingying XIAO E-mail:40892750@qq.com;jzrhitwh@sina.com;xiaoyingying504@126.com;517000314@qq.com;sunnyqiang737@163.com;lintingyu2003@sina.com;bitxue@foxmail.com;danny2006_2007@126.com
About author:GENG Chao was born in 1979. He is a Ph.D. candidate in Economics and Management School of Harbin Institute of Technology at Weihai. His research interests are national innovation system, innovation diffusion, and cloud manufacturing, et al. E-mail: 40892750@qq.com|QU Shiyou was born in 1969. He is a professor and Ph.D. supervisor in Economics and Management School of Harbin Institute of Technology at Weihai. His research interest is national innovation system. E-mail: jzrhitwh@sina.com|XIAO Yingying was born in 1987. She is a Ph.D. and an engineer of State Key Laboratory of Intelligent Manufacturing System Technology. Her research interests include modeling & simulation, cloud manufacturing and swarm intelligent algorithm. E-mail: xiaoyingying504@126.com|WANG Mei was born in 1979. She is a senior engineer of State Key Laboratory of Intelligent Manufacturing System Technology. Her research interests include national innovation system, innovation diffusion, and cloud manufacturing, et al. E-mail: 517000314@qq.com|SHI Guoqiang was born in 1979. He is a senior engineer of State Key Laboratory of Intelligent Manufacturing System Technology. His research interests include modeling & simulation and cloud manufacturing, et al. E-mail: sunnyqiang737@163.com|LIN Tingyu was born in 1984. He is a senior engineer of State Key Laboratory of Intelligent Manufacturing System Technology. His research interests include modeling & simulation and cloud manufacturing, et al. E-mail: lintingyu2003@sina.com|XUE Junjie was born in 1987. He is a Ph.D. and an engineer of Beijing Complex Product Advanced Manufacturing Engineering Research Center. His research interests include system modeling & simulation, web-based 3D applications, geometry compression and real-time rendering. E-mail: bitxue@foxmail.com|JIA Zhengxuan born in 1990. He is currently an engineer of State Key Laboratory of Intelligent Manufacturing System Technology. His research interests include modeling & simulation, automatic control theory and applications and evolutionary computation algorithms. E-mail: danny2006_2007@126.com
Supported by:
the National High-Tech R & D Program, China(2015AA042101);This work was supported by the National High-Tech R & D Program, China (2015AA042101)

Abstract

Abstract:

Cloud manufacturing is a specific implementation form of the "Internet + manufacturing" strategy. Why and how to develop cloud manufacturing platform (CMP), however, remains the key concern of both platform operators and users. A microscopic model is proposed to investigate advantages and diffusion forces of CMP through exploration of its diffusion process and mechanism. Specifically, a three-stage basic evolution process of CMP is innovatively proposed. Then, based on this basic process, a more complex CMP evolution model has been established in virtue of complex network theory, with five diffusion forces identified. Thereafter, simulations on CMP diffusion have been conducted. The results indicate that, CMP possesses better resource utilization, user satisfaction, and enterprise utility. Results of simulation on impacts of different diffusion forces show that both the time required for CMP to reach an equilibrium state and the final network size are affected simultaneously by the five diffusion forces. All these analyses indicate that CMP could create an open online cooperation environment and turns out to be an effective implementation of the "Internet + manufacturing" strategy.

Key words: complex network, cloud manufacturing, innovation diffusion, network effect, Gale-Shapley algorithm, cooperative game theory

Chao GENG, Shiyou QU, Yingying XIAO, Mei WANG, Guoqiang SHI, Tingyu LIN, Junjie XUE, Zhengxuan JIA. Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory[J]. Journal of Systems Engineering and Electronics, 2018, 29(2): 321-335.

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Category	Parameter	Description	Range of values	Initialization
Basic parameter	$N$	BA scale-free network size	1 000	—
	$m$0	Number of initial network nodes	3	—
	$m$	Number of connections between the newnode and the existing nodes	2	—
	$T$Max	Total evolution time steps of cloudmanufacturing network	200	—
	$pot$	Ratio of enterprises with production tasks	0.2	—
	$s$	Maximum task intensity	5	—
	$Qj$	Service quality level of enterprise$j$	randInt(1, 3)	—
	cos ${t_j}$	Service cost of enterprise$j$	rand(1, 2)	—
SDM strategy	$S - DStra$	Supply and demand matching strategy	0-Deferred-acceptance strategy1-Immediate-acceptance strategy	1
IEG strategy	$p$	The ratio of demanders/providersinitially guided online	0.1	—
IEG strategy	$GuidStra$	Guideline strategy	0-Provider priority1-Demander priority	1
Parameter relatedto PQIm	$\begin{array}{l}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} q0\\q\max \\{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} c\end{array}$	Initial quality of the platformMaximum quality of the platformLearning coefficient of the platform	22 – 120.05 – 0.2	—120.05
Parameter relatedto EPH	${r_i}$	Distribution of preferences ofenterprise i for the platform	Beta4 distribution of$N$(3, 3, ?14, 4)	—
	alpha	Ratio of DATs	0 – 1	0.5
	keytype	Ratio of KCPs	0 – 1	1
NEs parameter	$d$1	Direct network effect strength	1–5$d(t) = 0.5{\rm{ \times }}qt - 1$	1
	$d$2	Indirect network effect strength	1–5$d(t) = 0.5{\rm{ \times }}qt - 1$	1
	localstrength	Local network effect strength	1-5	1

	Framework	Operating mechanism	SDM strategy
CM	Widely connected throughresource service pool, centrallyoperated by operator	Complete resource schedulingand sharing through resourceservice pool	Matching throughout allenterprises; centralized resourceservice allocation by operator
NM	Locally connected	Partial resource scheduling andsharing limited amongconnected enterprises	Matching locally among connectedenterprises; decentralized resourceservice allocation by enterprises

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Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory

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