Abstract
In the collaborative production environment of manufacturing tasks, the evaluation of enterprise technical capability in advance has a direct impact on the high-performance collaboration between the supplier and the demander of production tasks. In order to solve the problem of efficiency and accuracy in the past evaluation methods, this paper applies the adaptive neuro fuzzy inference system (ANFIS) to the field of enterprise technical capability evaluation for the first time, and proposes an improved hybrid intelligent model SFAHP-ANFIS-PSO based on fuzzy theory. The model takes the four key indexes which have the deepest impact on enterprise technical capability evaluation as the input variables of the model, and the input data are evaluated by ANFIS with high accuracy. Spherical fuzzy analytic hierarchy process (SFAHP) calculated the weight of each evaluation index to preprocess the ANFIS model. Particle swarm optimization algorithm (PSO) continuously optimized the model parameters in the training process, shortened the model training time, improved the evaluation efficiency, and further improved the accuracy of the evaluation results. The experimental results show that the SFAHP-ANFIS-PSO model has high convergence speed and accuracy of results, and can be used to evaluate the technical capability of enterprises with high efficiency and high accuracy.
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References
Ansell, C., Gash, A.: Collaborative governance in theory and practice. J. Public Admin. Res. Theory 18(4), 543–571 (2007)
Choi, I., Moynihan, D.: How to foster collaborative performance management? Key factors in the US federal agencies. Public Manag. Rev. 21(10), 1538–1559 (2019)
Prashanth, K.D., Parthiban, P., Dhanalakshmi, R.: Evaluation of the performance and ranking of suppliers of a heavy industry by TOPSIS method. J. Sci. Ind. Res. 79(2), 144–147 (2020)
Haleem, A., Khan, S., Luthra, S., Varshney, H., Alam, M., Khan, M.I.: Supplier evaluation in the context of circular economy: a forward step for resilient business and environment concern. Bus. Strateg. Environ. 30(4), 2119–2146 (2021)
Zhou, J., Liu, Z., Li, J., Zhang, G.: The effects of collaboration with different partners: a contingency model. IEEE Trans. Eng. Manage. 68(6), 1546–1557 (2021)
Kutlu, G.F., Kahraman, C.: A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft. Comput. 24(6), 4607–4621 (2020)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Wang, W., Wang, J., Chen, C., Su, S., Chu, C., Chen, G.: A capability maturity model for intelligent manufacturing in chair industry enterprises. Processes 10(6) (2022)
Dou, Y., Xue, X., Wang, Y., Xue, W., Huangfu, W.: Evaluation of enterprise technology innovation capability in prefabricated construction in China. Constr. Innov. 22(4), 1059–1084 (2021)
Shi, L., Ding, X., Li, M., Liu, Y., Ahmad, M.: Research on the capability maturity evaluation of intelligent manufacturing based on firefly algorithm, sparrow search algorithm, and BP neural network. Complexity, 1–26 (2021)
Wang Z., Lu, J., Li, M., Yang, S., Wang, Y., Cheng, X.: Edge computing and blockchain in enterprise performance and venture capital management. Comput. Intell. Neurosci. 2914936 (2022)
Tuzkaya, G.: An intuitionistic fuzzy Choquet integral operator based methodology for environmental criteria integrated supplier evaluation process. Int. J. Environ. Sci. Technol. 10(3), 423–432 (2013)
Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Jaafari, A., Zenner, E.K., Panahi, M., Shahabi, H.: Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agric. For. Meteorol. 266–267, 198–207 (2019)
Harandizadeh, H., Armaghani, D.J.: Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Appl. Soft Comput. 99(1), 106904 (2021)
Golafshani, E.M., Behnood, A., Arashpour, M.: Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Constr. Build. Mater. 232, 117266 (2020)
Acknowledgements
This work was supported in part by the National Key R&D Program of China under Grant No. 2020YFB1707900 and 2020YFB1711800; the National Natural Science Foundation of China under Grant No. 62262074, U2268204 and 62172061; the Science and Technology Project of Sichuan Province under Grant No. 2022YFG0159, 2022YFG0155, 2022YFG0157.
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Liu, T., Ding, X., Jiang, Y., Hu, D. (2023). A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_46
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