Cloud Based Decision Making for Multi-agent Production Systems

1. Onori, M., Barata, J.: Evolvable production systems: new domains within mechatronic production equipment. In: 2010 IEEE International Symposium on Industrial Electronics, pp. 2653–2657. IEEE (2010)

   Google Scholar 

2. Tharumarajah, A.: Comparison of the bionic, fractal and holonic manufacturing system concepts. Int. J. Comput. Integr. Manuf. 9(3), 217–226 (1996)

   Article  Google Scholar 

3. Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: prosa. Comput. Ind. 37(3), 255–274 (1998)

   Article  Google Scholar 

4. Bi, Z.M., Lang, S.Y., Shen, W., Wang, L.: Reconfigurable manufacturing systems: the state of the art. Int. J. Prod. Res. 46(4), 967–992 (2008)

   Article  Google Scholar 

5. Leitão, P., Colombo, A.W., Karnouskos, S.: Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput. Ind. 81, 11–25 (2016)

   Article  Google Scholar 

6. Wang, L., Du, Z., Dong, W., Shen, Y., Zhao, G.: Hierarchical human machine interaction learning for a lower extremity augmentation device. Int. J. Soc. Robot. 11(1), 123–139 (2019)

   Article  Google Scholar 

7. Wu, D., Zhang, Y., Ourak, M., Niu, K., Dankelman, J., Vander Poorten, E.B.: Hysteresis modeling of robotic catheters based on long short-term memory network for improved environment reconstruction. IEEE Robot. Autom. Lett. 6(2), 2106–2113 (2021)

   Google Scholar 

8. Torayev, A., Schultz, T.: Interactive classification of multi-shell diffusion MRI with features from a dual-branch CNN autoencoder. In: EG Workshop on Visual Computing for Biology and Medicine (2020)

   Google Scholar 

9. Tang, T., Hu, T., Chen, M., Lin, R., Chen, G.: A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions. In: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, p. 0954406220902181 (2020)

   Google Scholar 

10. Vincent Wang, X., Xu, X.W.: An interoperable solution for cloud manufacturing. Robot. Comput.-Integr. Manuf. 29(4), 232–247 (2013)

    Article  Google Scholar 

11. Wooldridge, M.: An Introduction to Multiagent Systems. John Wiley & Sons (2009)

    Google Scholar 

12. Bond, A.H., Gasser, L.: Readings in Distributed Artificial Intelligence. Morgan Kaufmann (2014)

    Google Scholar 

13. Botti, V., Omicini, A., Mariani, S., Julian, V.: Multi-agent Systems. MDPI-Multidisciplinary Digital Publishing Institute (2019)

    Google Scholar 

14. Adeyeri, M.K., Mpofu, K., Olukorede, T.A.: Integration of agent technology into manufacturing enterprise: a review and platform for industry 4.0. In: 2015 International Conference on Industrial Engineering and Operations Management (IEOM), pp. 1–10. IEEE (2015)

    Google Scholar 

15. Li, Z., Jiang, X., Yao, S., Li, D.: Research on collaborative control method of manufacturing process based on distributed multi-agent cooperation. In: 2018 11th International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 41–46. IEEE (2018)

    Google Scholar 

16. Li, D., Jiang, X., Wei, X.: Research on manufacturing process control based on multi-agent-system. In: 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 1306–1309. IEEE (2018)

    Google Scholar 

17. Răileanu, S., Borangiu, T., Morariu, O.: Multi-agent solution for automated part supply in robotized holonic manufacturing. In: Rodić, A., Borangiu, T. (eds.) RAAD 2016. AISC, vol. 540, pp. 211–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49058-8_23

    Chapter  Google Scholar 

18. Vatankhah Barenji, A., Vatankhah Barenji, R.: Improving multi-agent manufacturing control system by indirect communication based on ant agents. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 231(6), 447–458 (2017)

    Google Scholar 

19. Gwiazda, A., Sękala, A., Banaś, W.: Modeling of a production system using the multi-agent approach. In: IOP Conference Series: Materials Science and Engineering, vol. 227, p. 012052. IOP Publishing (2017)

    Google Scholar 

20. Blesing, C., Luensch, D., Stenzel, J., Korth, B.: Concept of a multi-agent based decentralized production system for the automotive industry. In: Demazeau, Y., Davidsson, P., Bajo, J., Vale, Z. (eds.) PAAMS 2017. LNCS (LNAI), vol. 10349, pp. 19–30. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59930-4_2

    Chapter  Google Scholar 

21. Büth, L., Broderius, N., Herrmann, C., Thiede, S.: Introducing agent-based simulation of manufacturing systems to industrial discrete-event simulation tools. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 1141–1146. IEEE (2017)

    Google Scholar 

22. Leitao, P., Karnouskos, S., Ribeiro, L., Lee, J., Strasser, T., Colombo, A.W.: Smart agents in industrial cyber-physical systems. Proc. IEEE 104(5), 1086–1101 (2016)

    Article  Google Scholar 

23. Barata, J., Camarinha-Matos, L.M.: Coalitions of manufacturing components for shop floor agility-the Cobasa architecture. Int. J. Networking Virtual Organ. 2(1), 50–77 (2003)

    Article  Google Scholar 

24. Peres, R.S., Rocha, A.D., Leitao, P., Barata, J.: Idarts-towards intelligent data analysis and real-time supervision for industry 4.0. Comput. Ind. 101, 138–146 (2018)

    Google Scholar 

25. Rocha, A.D., Peres, R.S., Flores, L., Barata, J.: A multiagent based knowledge extraction framework to support plug and produce capabilities in manufacturing monitoring systems. In: 2015 10th International Symposium on Mechatronics and its Applications (ISMA), pp. 1–5. IEEE (2015)

    Google Scholar 

26. Baer, S., Bakakeu, J., Meyes, R., Meisen, T.: Multi-agent reinforcement learning for job shop scheduling in flexible manufacturing systems. In: 2019 Second International Conference on Artificial Intelligence for Industries (AI4I), pp. 22–25. IEEE (2019)

    Google Scholar 

27. Rokhforoz, P., Gjorgiev, B., Sansavini, G., Fink, O.: Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market. arXiv preprint arXiv:2002.12217 (2020)

28. Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA: Electronic Letters on Computer Vision and Image Analysis, pp. 1–22 (2008)

    Google Scholar 

29. Neogi, N., Mohanta, D.K., Dutta, P.K.: Review of vision-based steel surface inspection systems. EURASIP J. Image Video Process. 2014(1), 1–19 (2014). https://doi.org/10.1186/1687-5281-2014-50

    Article  Google Scholar 

30. Pernkopf, F., O’Leary, P.: Visual inspection of machined metallic high-precision surfaces. EURASIP J. Adv. Signal Process. 2002(7), 1–12 (2002)

    Article  Google Scholar 

31. Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manuf. Syst. 48, 144–156 (2018)

    Article  Google Scholar 

32. Wyns, J.: Reference architecture for holonic manufacture: the key to support evolution and reconfiguration. Unpublished PhD thesis, Katholieke Universiteit Leuven, Leuven (1999)

    Google Scholar 

33. Elsken, T., Metzen, J.H., Hutter, F., et al.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(55), 1–21 (2019)

    MathSciNet  MATH  Google Scholar 

34. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.: Automl for large scale image classification and object detection. Google AI Blog 2, 2017 (2017)

    Google Scholar 

35. Dabhi, R.: casting product image data for quality inspection. In: https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-product (2020)

Download references