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Research on the Cooperative Behavior in Cloud Manufacturing

  • Ping LouEmail author
  • Cui Zhu
  • Xiaomei Zhang
  • Xuemei Jiang
  • Zhengying Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)

Abstract

With the development of information and network technology, a new type of manufacturing paradigm Cloud Manufacturing (CMg) is emerged. In the CMg environment, geographically distributed various manufacturing resources (MgSs) and manufacturing capabilities managed by different companies are encapsulated as different manufacturing services under the support of cloud computing, Internet of Things and virtualization technologies. CMg can provide on-demand MgSs for the manufacturing tasks (MgTs) of different customers in the network manufacturing environment. One MgT usually needs different MgSs owned by different companies to form a coalition for working together to finish it. However, being an autonomous entity, each MgS generally makes decisions in light of its own interests, so it is difficult to maximize the collective interests of the coalition. The cooperation among the MgSs in the same coalition is an effective way to maximize the collective interests. Hence, how to motivate MgSs to cooperate mutually is been paid more attention in cloud manufacturing environment. In the paper, the evolutionary game theory and the learning automaton are employed to model the decision-making process of MgSs. And a punishment mechanism is introduced to incentivize the mutual cooperation of MgSs. Furthermore, the Blockchain as a data storage structure is adopted to record the behaviors of the MgSs to prevent from falsifying their feedback. At last, the agent-based modeling is used to model and simulate the process of MgSs working together. The simulating results reveal that the punishment mechanism is effective in promoting the cooperation among MgSs from various perspectives.

Keywords

Public good game Incentive mechanism Learning automaton Agent-based modeling and simulating 

Notes

Acknowledgements

The authors would like to acknowledge funding support from the National Natural Science Foundation Committee of China under Grant No. 51475347 and the Major Project of Technological Innovation Special Fund of Hubei Province Grant No. 2016AAA016, as well as the contributions from all collaborators within the projects mentioned. We would also like to thank Wuhan University of Technology, People’s Republic of China in supporting this work.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information EngineeringWuhan University of TechnologyWuhanChina

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