Table of Content

01 December 2021, Volume 33 Issue 4

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A Grey Target Decision-Based Risk Evaluation and Prioritization Method for FMEA with Interval Grey Number

Dandan Wang, Lirong Jian, Sifeng Liu, Shuaishuai Fu

2021, 33(4):  1-15. 

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As an effective risk evaluation method, the Failure mode and effect analysis (FMEA) has been widely adopted to assist in controlling risk and enhancing the reliability of the system in a variety of workplaces. Nevertheless, the current risk prioritization approach for FMEA is insufficient to fully handle the deviation among risk evaluation information from experts with different professional backgrounds in the existing literature. With the intention of remedying this gap, this study put forward a novel risk evaluation and prioritization method for FMEA based on grey theory. Firstly, in order to cope with the deviation in risk evaluation information, which is caused by extreme information from group decision-making, the interval grey number is used as language variables for risk evaluation by experts with different professional backgrounds. Secondly, consider the correlation among risk factors, the important weight of these risk factors is determined by grey relational analysis and entropy measurement method. Thirdly, the grey target decision approach is adopted to decide the priority of these failure modes. Finally, an instance of an aircraft landing system is used to verify the viability and application of this proposed method.


Assessment of China’s Aerospace Industry Sustainable Development Capability

Chaoqing Yuan, Xiuqin Wang

2021, 33(4):  16-31. 

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The aerospace industry is very important due to its characteristic of dual-use. The aerospace industry is facing greater difficulties in terms of sustainability of both resource input and market expansion than the early years. This paper defines the aerospace industry's sustainable development capability, and its connotations are analyzed from four aspects. On this basis, the aerospace industry sustainable development capability assessment index system is constructed, consisting of 15 indexes whose weights are obtained by using the AHP method. With this index system, the sustainable development capability of China's aerospace industry from 2012 to 2018 is assessed by using the method of grey fixed-weight clustering where grey classes including High, Medium, and Low, and it is found that the sustainable development capability of China's aerospace industry belongs to High or Medium grey classes, but the problems of industrial ecology and industrial system flexibility are rather prominent. Accordingly, some suggestions are put forward.


Evaluation and Analysis of Key Influencing Factors of Scientific Research Efficiency of "Double First-Class" Universities in China

Hongwei Li, Haiyang Jiang

2021, 33(4):  32-45. 

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Scientific research efficiency is an important index of the ability of an institution to comprehensively utilize scientific research resources, especially in "Double First-Class" Universities in China. It is significant for guiding the optimal allocation of universities’ scientific research resources as they are constructed. In this study, an evaluation index system for "Double First-Class" Universities’ scientific research efficiency is constructed based on the characteristics of their scientific research activities. Their efficiency is evaluated scientifically through an output-oriented two-stage super efficiency Data Envelopment Analysis model, and key influencing factors of efficiency are identified using the improved Grey Correlation Analysis method. 27 "Double First-Class" Universities’ scientific research efficiencies are calculated based on data from 2016 to 2018. The results show that: (1) The two-stage DEA model considers the hysteresis of scientific activities and can calculate the decomposed efficiency. (2) The overall scientific research efficiency of "Double First-Class" Universities in China is low, and there is a wide range of efficiencies among the universities. (3) Number of highly cited papers and the total number of published papers are the key influencing factors of scientific research efficiency. Based on these results, some suggestions are put forward to improve the scientific research efficiency of "Double First-Class" Universities in China.


Predictive Model for the Number of Elevator Failures Based on the Residual Error Correction Model and a GM (1,1)-Markov Chain

Xin Feng, JunCheng Jiang, WenFeng Wang, YueGui Feng

2021, 33(4):  46-60. 

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The rapid increase in the number of elevators in China demonstrates the indispensable role in daily life. We propose a prediction model based on the residual error correction model and the Markov chain for forecasting the number of elevator failures. First, we developed a traditional grey model (GM) (1,1) by using data from January to October; we then used the residual errors to correct the GM(1,1), thereby creating an error-correction GM (C-GM). The experimental results indicated that after two corrections, this model achieved a variance ratio C was 0.26, and a small error probability P was 1.0. Finally, we created a Markov-C-GM by combining the C-GM with the Markov chain to predict the number of elevator failures for the subsequent 2 months. We also compared this model with the autoregressive integrated moving average (ARIMA) model regarding their ability to predict the number of elevator failures in December; the results demonstrated that the Markov-C-GM outperformed the ARIMA model in terms of its ability to process small-sample data.


The Influence of Random Disturbance on DGM(1,1) Model

Lingling Pei, Junjie Yan, Yuanyuan Guo

2021, 33(4):  61-74. 

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At present, the DGM(1,1) model has many applications, which makes the model have a lot of improvement in accuracy and robustness. However, for these current applications, the impact of random disturbances on the model is not considered. A white noise is introduced in the traditional DGM(1,1) model by referring to tthe idea of random difference. The iterative method is used to solve the DGM(1,1) model which introduces the white noise sequence, and the corresponding analysis formula is obtained. According to this analysis, different values of β_1 will cause random disturbances to have different effects on the model. The cases with different β_1 values are used for comparative analysis. In the more general case of this model, what will the properties of the model differ after adding the random disturbance? The results show that when |β_1|<1, the variance of the model converges gradually under the influence of random disturbance, and the model is relatively stable, while when |β_1|>1, the variance increases exponentially, and the model is in an unstable state.


Interval Number Time Series Forecasting Based on GM (1, 1) and Nonlinear Regression

Xiangyan Zeng, Yunjie Mei, Shuli Yan

2021, 33(4):  75-88. 

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GM (1, 1) model is suitable for the series with exponential growth and small fluctuation, but the prediction accuracy of the series with parabolic or saturated development trends is not high. Parabolic growth sequences are widely found in practical problems. For example, the annual GDP of some provinces in China grew rapidly in the early stage but slowed down in the later stage, presenting a parabolic or saturated development trend. In order to improve the prediction effect of grey model on exponential and parabolic sequences, the sum of a quadratic polynomial and GM (1, 1) model is proposed as a new model (NRGM (1, 1)). Furthermore, the matrix model (MINRGM (1, 1)) of NRGM (1, 1) is proposed, which is directly applicable to interval number sequences. The prediction formula of the model is obtained based on Cramer's law. The MINRGM (1, 1) model is used to forecast China's railway passenger volume, civil aviation passenger volume, total passenger volume, and the GDP of Hebei province, and the GDP of Hebei province presents a parabolic development trend. Compared with the competition models, the MINRGM (1, 1) model achieves better prediction results.


A Grey Information Evolution Theory From the Perspective of Topological Structures

Jianghui Wen, Chaozhong Wu, Xinping Xiao, Yu Shi

2021, 33(4):  89-101. 

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For analyzing the evolution process of grey information, this paper aims to establish a mathematical structure theory of grey information which is limited and insufficient. Firstly, grey space theory is built to describe the connotation and extension of grey information, proving that grey information during evolution satisfies the grey kernel attribute invariance and grey information measure non-increasing principles. Secondly, the dynamic evolution of grey information is calculated based on grey information measurement. Then, complete steps of the grey clustering method based on grey information evolution theory are given. Our method can be extended to a general uncertain system.


Game Network and Equilibrium Analysis of Mixed-Ownership Asset Supervision in a Grey Information Context

Jing Wang, Yufeng Song, Xiaoyu Yang, Na Zhang, Zhigeng Fang

2021, 33(4):  102-128. 

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The supervision of state-owned assets is not only an important issue in the reform of state-owned assets but also the key to the preservation and appreciation of state-owned assets. This research combines the generality of modern enterprise governance with the Chinese characteristics of adhering to the Party’s leadership over state-owned enterprises and proposes a new regulatory framework for state-owned assets. By combining the principal-agent chain relationship of mixed-ownership enterprises, a grey principal-agent model under the incomplete information on agents’ actions is constructed, and based on the constructed model, and an optimization algorithm of the grey principal-agent problem is designed by employing the cooperative game equilibrium solution. The results show that: under the condition of constant supervision intensity, the optimal effort level of public and non-public managers of state-owned asset appreciation is inversely proportional to their effort cost coefficient, that is, the lower the effort cost coefficients of the public and non-public managers, the higher the optimal effort levels, thereby the higher the level of asset appreciation. In addition, the asset appreciation incentive coefficient of public and non-public managers always decreases with increasing supervision intensity. With the increase in supervision intensity, the income of the enterprise always increases, but the net profit decreases after exceeding the critical point of optimal supervision intensity, which means that the optimal supervision intensity to maximize the interests cannot guarantee the maximization of asset appreciation. By introducing the collaborative regulatory framework, the greyness of the three participants’ income decreases, and the minimum incomes of the three participants under supervision can be guaranteed to be higher than that without supervision over a wide range.


The Superiority Analysis of Two Kinds of Discrete Grey Model with Fractional-Order Accumulation

Jiefang Liu, Pumei Gao, Shanshan Li, Bingjun Li

2021, 33(4):  129-137. 

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The basis of the grey forecasting model is the accumulation of original data. The rule of system change can be highlighted through the accumulation of data. The fractional-order accumulation discrete grey forecasting model presents a fractional-order accumulation method for raw data. The stability of the model can be effectively increased by fractional-order accumulation. The fractional-order reverse accumulation discrete grey forecasting model can make full use of the new information of the system, which is more consistent with the principle of new information priority. In this paper, the characteristics and advantages of the two models are compared theoretically and verified by an example. We found that when the old data of the original data were disturbed, the stability of the discrete grey forecasting model with reverse fractional-order accumulation was good.


Multi-attribute Annular Grey Target Decision-making Based on Density Weighted Operators

Sandang Guo, Bingjun Li, Fenyi Dong

2021, 33(4):  138-149. 

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An annular grey target decision-making method based on density weighted operator is proposed in view of the present situation that the existing grey target decision-making methods don’t divide the grey target into different groups and couldn't scientifically solve the decision problems that the distribution of the evaluation information is inconsistent. According to different dentistry, the evaluation information is divided into different categories by orderly incremental method, and a nonlinear programming model is established for calculating the weight vector. On this basis, a grey decision-making method with one bull's-eye and multiple annular gaps is defined, and the corresponding weight vector is given. It is made the evaluation results more reasonable and practical by using twice aggregation of the evaluation information inside the rings and between the rings. Finally, two cases are given to illustrate the feasibility and validity of the proposed method.