Skip to main content

Research on the Cooperative Behavior in Cloud Manufacturing

  • Conference paper
  • First Online:
Methods and Applications for Modeling and Simulation of Complex Systems (AsiaSim 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 946))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wu, D., Greer, M.J., Rosen, D.W., et al.: Cloud manufacturing: strategic vision and state-of-the-art. J. Manuf. Syst. 32, 564–579 (2013)

    Article  Google Scholar 

  2. Peng, C., Meng, Y.: Empirical study of manufacturing enterprise collaboration network: formation and characteristics. Robot. Comput.-Integr. Manuf. 42, 49–62 (2016)

    Article  Google Scholar 

  3. Li, W., Zhu, C., Wei, X., et al.: Characteristics analysis and optimization design of entities collaboration for cloud manufacturing. Concurr. Comput.: Pract. Exp. 29, e3948 (2017)

    Article  Google Scholar 

  4. Cui, G., Wang, Z., Yang, Y., et al.: Heterogeneous game resource distributions promote cooperation in spatial prisoner’s dilemma game. Phys. A: Stat. Mech. Appl. 490, 1191–1200 (2018)

    Article  Google Scholar 

  5. Scatà, M., Di Stefano, A., La Corte, A., et al.: Combining evolutionary game theory and network theory to analyze human cooperation patterns. Chaos, Solitons Fractals 91, 17–24 (2016)

    Article  MathSciNet  Google Scholar 

  6. Xia, C., Ding, S., Wang, C., et al.: Risk analysis and enhancement of cooperation yielded by the individual reputation in the spatial public goods game. IEEE Syst. J. 11, 1516–1525 (2017)

    Article  Google Scholar 

  7. Yang, H., Chen, X.: Promoting cooperation by punishing minority. Appl. Math. Comput. 316, 460–466 (2018)

    MathSciNet  Google Scholar 

  8. Wu, Y.E., Zhang, B., Zhang, S.: Probabilistic reward or punishment promotes cooperation in evolutionary games. Chaos, Solitons Fractals 103, 289–293 (2017)

    Article  MathSciNet  Google Scholar 

  9. Szolnoki, A., Perc, M.: Effectiveness of conditional punishment for the evolution of public cooperation. J. Theor. Biol. 325, 34–41 (2013)

    Article  MathSciNet  Google Scholar 

  10. Narendra, K.S., Thathachar, M.A.L.: Learning automata - a survey. SMC 4, 323–334 (1974)

    MathSciNet  MATH  Google Scholar 

  11. Hasanzadeh-Mofrad, M., Rezvanian, A.: Learning automata clustering. J. Comput. Sci. 24, 379–388 (2018)

    Article  MathSciNet  Google Scholar 

  12. Moradabadi, B., Meybodi, M.R.: Link prediction in weighted social networks using learning automata. Eng. Appl. Artif. Intell. 70, 16–24 (2018)

    Article  Google Scholar 

  13. Zhang, S., Zhang, Z., Wu, Y.E., et al.: Tolerance-based punishment and cooperation in spatial public goods game. Chaos, Solitons Fractals 110, 267–272 (2018)

    Article  MathSciNet  Google Scholar 

  14. Gao, M., Chen, L., Li, B., et al.: Projection-based link prediction in a bipartite network. Inf. Sci. Int. J. 376, 158–171 (2017)

    Google Scholar 

  15. Zhao, J., Liu, Q., Wang, X.: Competitive dynamics on complex networks. Sci. Rep. 4, 5858 (2015)

    Article  Google Scholar 

  16. Li, M., Song, H., Zhang, L., et al.: Maintenance of cooperation in a public goods game: a new decision-making criterion with incomplete information. Chin. Sci. Bull. 57, 579–583 (2012)

    Article  Google Scholar 

  17. Huang, K., Chen, X., Yu, Z., et al.: Heterogeneous cooperative belief for social dilemma in multi-agent system. Appl. Math. Comput. 320, 572–579 (2018)

    MathSciNet  Google Scholar 

  18. Lu, K., Wang, S., Xie, L., Li, M.: Study of self-adaptive strategy based incentive mechanism in structured P2P system. In: Huang, D.-S., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS (LNAI), vol. 9773, pp. 658–670. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42297-8_61

    Chapter  Google Scholar 

  19. Levy, N., Klein, I., Ben-Elia, E.: Emergence of cooperation and a fair system optimum in road networks: a game-theoretic and agent-based modelling approach. Res. Transp. Econ. (2017)

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ping Lou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lou, P., Zhu, C., Zhang, X., Jiang, X., Li, Z. (2018). Research on the Cooperative Behavior in Cloud Manufacturing. In: Li, L., Hasegawa, K., Tanaka, S. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2018. Communications in Computer and Information Science, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-13-2853-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-2853-4_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2852-7

  • Online ISBN: 978-981-13-2853-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics