Cooperative Quality Evaluation of Supply Chain using Structural Characteristics

doi:10.23940/ijpe.20.05.p11.775783

Abstract

Abstract:

There is a quality benefit error in the block collaborative supply chain that is affected by the environmental factors of the supply market. In order to improve the ability of collaborative quality evaluation, a supply chain collaborative quality evaluation method based using structural characteristics is proposed. The sampling model of collaborative quality characteristic information of block collaborative supply chain is constructed. According to the quantitative recurrent analysis results of the collaborative quality sample data, block chain information fusion is carried out, and the association rules fusion feature distribution parameter set of block collaborative supply chain collaborative quality panel data is extracted. The collaborative quality data fusion of block collaborative supply chain is carried out by using the method of random probability density feature detection. Combined with the method of piecewise linear estimation, the statistical feature quantity of supply chain collaborative quality evaluation is constructed. According to the prior sample quantitative recurrent analysis results of supply chain collaborative quality evaluation, the collaborative quality feature quantity is analyzed, and the cooperative quality feature quantity of block collaborative supply chain is extracted. The structure feature extraction and fusion clustering method are used for information clustering. According to the distributed fusion results of characteristics, the collaborative quality of supply chain is evaluated. The simulation results show that the proposed method has high accuracy and good confidence in the evaluation of supply chain collaborative quality, and it improves the ability of collaborative quality control of block collaborative supply chain.

Key words: fuzzy C-means, clustering, block cooperative supply chain, cooperative quality evaluation

Aidi Xu, and Yunfeng Shang. Cooperative Quality Evaluation of Supply Chain using Structural Characteristics [J]. Int J Performability Eng, 2020, 16(5): 775-783.

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Figures/Tables 5

References 15.

1.	H. Chen, P. Chen, H. S. Yang, et al., “Empirical Analysis of Offshore and Onshore RMB Interest Rate Pricing: based on the Spillover Index and its Dynamic Path,” International Financial Research, Vol. 350, No. 6, pp. 86-96, 2016
2.	Y. Lin, X. L. Zhu, Z. G. Zheng, Z. Dou,R. L. Zhou, “The Individual Identification Method of Wireless Device based on Dimensionality Reduction and Machine Learning,”The Journal of Supercomputing, Vol. 75, pp. 3010-3027, 2019
3.	F. Marcel, “Capital Flows Push Versus Pull Factors and the Global Financial Crisis,” Journal of International Economics, Vol. 88, No. 2, pp. 341-356, 2011
4.	H. Taguchi, P. Sahoo,G. Nataraj, “Capital Flows and Asset Prices: Empirical Evidence from Emerging and Developing Economies,” International Economics, Vol. 141, No. 5, pp. 1-14, 2015
5.	Y. Li, “Research on the Construction of the Service Supply Chain for the Circulation of Agricultural Products,” Journal of Southwest University of Science and Technology (Philosophy and Social Sciences), Vol. 34, No. 3, pp. 82-88, 2017
6.	A. T. D.Almeida, F. J. T. E. Fernando, and J. Fong, “Standards for Efficiency of Electric Motors,” IEEE Industry Applications Magazine, Vol. 17, No. 1, pp. 12-19, 2011
7.	A. Akrad, M. Hilairet,D. Diallo.“Design of a Fault-Tolerant Controller based on Observers for a PMSM Drive,” IEEE Transactions on Industrial Electronics, Vol. 58, No. 4, pp. 1416-1427, 2011
8.	A. Bellini, F. Filippetti, C. Tassoni,G. A. Capolino, “Advances in Diagnostic Techniques for Induction Machines,” IEEE Transactions on Industrial Electronics, Vol. 55, No. 12, pp. 4109-4126, 2008
9.	Y. Tu, Y. Lin, J. Wang,J. U. Kim, “Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification,” CMC-Computers Materials and Continua, Vol. 55, No. 2, pp. 243-254, 2018
10.	H. Berriri, W. Naouar, I. Bahri, et al., “Field Programmable Gate Array-based Fault-Tolerant Hysteresis Current Control for AC Machine Drives,” IET Electric Power Applications, Vol. 6, No. 3, pp. 181-189, 2012
11.	S. K. E.Khil, I. Jlassi, J. O. Estima, et al., “Current Sensor Fault Detection and Isolation Method for PMSM Drives, using Average Normalised Currents,” Electronics Letters, Vol. 52, No. 17, pp. 1434-1436, 2016
12.	G. Yang and S. Liu, “Distributed Cooperative Algorithm for k-M Set with Negative Integer k by Fractal Symmetrical Property,” International Journal of Distributed Sensor Networks, Vol. 10, No. 5, 2014
13.	X. J. Ni, A. M. Gole, C. Y. Zhao,C. Y. Guo, “An Improved Measure of ac System Strength for Performance Analysis of Multi-Infeed HV dc Systems Including VSC and LCC Converters,” IEEE Transactions on Power Delivery, Vol. 33, No. 1, pp. 169-178, 2018
14.	Y. Amirat and A. MvNCH, “On the Controllability of an Advection-Diffusion Equation with Respect to the Diffusion Parameter: Asymptotic Analysis and Numerical Simulations,” Acta Mathematicae Applicatae Sinica, English Serie, Vol. 35, No. 1, pp. 54-110, 2019
15.	M. Liu, S. Liu, W. Fu,J. T. Zhou, “Distributional Escape Time Algorithm based on Generalized Fractal Sets in Cloud Environment,” Chinese Journal of Electronics, Vol. 24, No. 1, pp. 124-127, 2015

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