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.
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References
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)
Peng, C., Meng, Y.: Empirical study of manufacturing enterprise collaboration network: formation and characteristics. Robot. Comput.-Integr. Manuf. 42, 49–62 (2016)
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)
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)
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)
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)
Yang, H., Chen, X.: Promoting cooperation by punishing minority. Appl. Math. Comput. 316, 460–466 (2018)
Wu, Y.E., Zhang, B., Zhang, S.: Probabilistic reward or punishment promotes cooperation in evolutionary games. Chaos, Solitons Fractals 103, 289–293 (2017)
Szolnoki, A., Perc, M.: Effectiveness of conditional punishment for the evolution of public cooperation. J. Theor. Biol. 325, 34–41 (2013)
Narendra, K.S., Thathachar, M.A.L.: Learning automata - a survey. SMC 4, 323–334 (1974)
Hasanzadeh-Mofrad, M., Rezvanian, A.: Learning automata clustering. J. Comput. Sci. 24, 379–388 (2018)
Moradabadi, B., Meybodi, M.R.: Link prediction in weighted social networks using learning automata. Eng. Appl. Artif. Intell. 70, 16–24 (2018)
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)
Gao, M., Chen, L., Li, B., et al.: Projection-based link prediction in a bipartite network. Inf. Sci. Int. J. 376, 158–171 (2017)
Zhao, J., Liu, Q., Wang, X.: Competitive dynamics on complex networks. Sci. Rep. 4, 5858 (2015)
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)
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)
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
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)
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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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
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DOI: https://doi.org/10.1007/978-981-13-2853-4_19
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