Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

Alinani, Karim; Liu, Deshun; Zhou, Dong; Wang, Guojun

doi:10.1109/access.2020.3045008

Cited by 11 publications

(5 citation statements)

References 87 publications

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“…At present, scholars continue to explore the problem of cloud manufacturing SCO, and have achieved certain research results [7], [8]. Cloud manufacturing SCO is a multivariable and uncertain decision-making problem.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Guo

Niu

2023

IEEE Access

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In order to solve the problems such as the dynamic change of historical attribute service evaluation indicators, the lack of comprehensive consideration of the interest needs of all cloud manufacturing participants, and the strong subjectivity of the composition optimization results in the process of cloud manufacturing service composition. Taking the demands of service demanders, platform operators and service providers as constraints, this paper constructs a multi-objective optimization model of cloud manufacturing service composition that comprehensively considers multi-agent interests, and introduces the time decay function to deal with the service evaluation indicators with historical attributes, which reduces the impact of the dynamic changes of service evaluation indicators. Secondly, this paper adopts the NSGA-II with the elite selection strategy to solve the cloud manufacturing service composition optimization (SCO) model, and uses the grey target decision-making method to select the optimal solution from the Pareto solutions obtained by the NSGA-II, which avoids the problem of strong subjectivity in service composition decision-making. Finally, through case analysis, it was found that the bull's-eye distance of the optimal solution obtained by the NSGA-II was reduced by at least 34.95% compared to the genetic algorithm, verifying the feasibility of the optimization model and the effectiveness of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Guo

Niu

2023

IEEE Access

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“…Game theory has experienced the transition from classical game theory [4] to evolutionary game theory [5] and then to spatial evolutionary game theory [6]. At present, the research related with spatial evolutionary game is mainly divided into the following directions: (1) The influence of network structure on evolutionary dynamics; (2) The influence of strategy updating rules on evolutionary dynamics; (3) In the case of co-evolution of network structure and game dynamics, exploring and designing appropriate mechanisms to promote the emergence of cooperation.…”

Section: A Spatial Evolutionary Game Theorymentioning

confidence: 99%

“…CMfg is a service-oriented networked manufacturing paradigm, in which geographically distributed various manufacturing resources and capabilities are encapsulated as MSs [1][2][3]. Since CMfg is proposed, researches on CMfg have been taken great attention by academia and industries, and also a series of achievements have been made in the theoretical framework, key technology, the platform and architecture [4][5][6]. In CMfg paradigm, some MSs are able to provide with on-demand services for various MTs.…”

Section: Introductionmentioning

confidence: 99%

Cooperation Emergence of Manufacturing Services in Cloud Manufacturing With Agent-Based Modeling and Simulating

Lou

Zhu

et al. 2021

IEEE Access

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With the development of information and network technology, a new type of manufacturing paradigm, known as a service-oriented Cloud Manufacturing (CMfg), is emerged. In the CMfg paradigm, geographically distributed various manufacturing resources authorized by different companies are encapsulated as different manufacturing services (MSs) supported by the enabling technologies, such as cloud computing, Industrial Internet of Things, virtualization technologies, etc. MSs are provided according to the demands of manufacturing tasks (MTs) presented by various customers via the IoT (Internet of Things) in CMfg paradigm. Generally, one MS may complete one MT, but mostly one MT needs more MSs to complete it cooperatively. Furthermore, these MSs are perhaps controlled and managed by different enterprises. As an autonomous entity, each MS generally makes decision in light of its own interests, and their own interests generally have conflicts with the others' and the collective ones. To solve the conflicts through MSs' cooperation is an efficient and low-cost method to maximize both individual and collective interests. Hence, the influences of individual decision-making behaviors on cooperation are explored and discussed in CMfg paradigm. The evolutionary game theory and agent-based modeling and simulating method are employed to model and simulate individual behaviors of MSs during the process of forming the coalition for completing MTs. In addition, the learning mechanism of MSs is discussed in the decision-making process. The simulation results show that decision behaviors of MSs are positively influenced by the information transparency of the CMfg environment and the learning mechanism of MSs effects on the cooperative emergence.

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“…When an enterprise builds its own virtual enterprise in the cloud, it needs to virtualize and map the enterprise's manufacturing resources and capabilities to the cloud manufacturing service platform [19][20][21]. Faced with the problems of incompatible interfaces of a large number of heterogeneous manufacturing resources and inconsistent data formats, how to achieve the standardized description of manufacturing resources is the key to the efficient operation of the cloud manufacturing service platform.…”

Section: Onlinevirtualenterprisementioning

confidence: 99%

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

Shi

Tang

Zou³

2021

Teh. vjesn.

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Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

Cited by 11 publications

References 87 publications

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Cooperation Emergence of Manufacturing Services in Cloud Manufacturing With Agent-Based Modeling and Simulating

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

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