Table of Content

01 June 2021, Volume 33 Issue 2

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Multi-attribute Grey Relational Similarity Measure Evaluation Method for Weapon System Performance Based on Entropyweight

Wenguang Yang , Yunjie Wu, Shu Wang

2021, 33(2):  1-13. 

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In the current study, based on grey relational similarity measure and entropy-weight, a comprehensive grey relational similarity measure method for multi-attribute decision making (MADM) problem to evaluate the alternatives has been proposed. A new algorithm based on the same attribute value's comparison and different attribute value's synthesis for the MADM problems is also established. In this algorithm, the ideal attribute values are selected according to attribute properties. Later, the grey relational similarity measure method is used to calculate the grey value between the attribute value and the ideal attribute value in the same attribute. To compare the alternatives, the entropy-weight method is applied to determine the weights for different attributes objectively. Finally, we take the multi-attribute weapon system problem as an example to illustrate the effectiveness and validity of the proposed method. The example verification results show that the method constructed in this paper has higher credibility, and the rank reversal problem has also passed the test.

A novel grey model for multi-regional macro-data forecasting by considering spatial correlation and actual-state rolling

Ying Zhu, Yaping Li, Tangbin Xia, Lifeng Xi

2021, 33(2):  14-28. 

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Accurate prediction of regional development trend is important to regional planning and coordinated development in China. It provides a basis for decision-makings on the resource balance in multi-regional integration. However, due to the limitation of macro data and the influence of multi-regional correlation, the prediction accuracy of the existing forecasting methods in multi-regional macro-data forecasting is reduced. To overcome these problems, an improved grey model is proposed in this study. Firstly, a new spatial weight matrix is constructed based on the grey correlation analysis to define the spatial effect of multiple regions. Then, an actual-state rolling spatial-effect weighted grey model (ARSWGM) is developed considering the spatial interactions and the actual-state rolling mechanism. Finally, the proposed model is validated by the forecasting of manufacturing quality level of representative provinces in the process of regional coordinated development in China. The result shows that the proposed model demonstrates the best predicting performance compared with the classical grey forecasting models, indicating the advantages of this proposed model in multi-regional macro-data forecasting. Furthermore, this model can also be applied for a broader range of multi-regional limited macro-data forecasting.

On The Model of Industrial Structure Coordination Degree and Optimization Planning of Industrial Structure in Jiangsu Province and China

Sifeng Liu, Jing Deng

2021, 33(2):  29-38. 

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Industrial structure is the result of the allocation of economic resources of a country or a region, and to a large extent determines the efficiency of the use of economic resources. The process of national or regional economic growth is the dynamic evolution process of industrial structure from uncoordinated to coordinated, from lower-level coordination to higher-level coordination, that is, the process of continuous optimization of industrial structure. The concepts of industrial structure deviation and industrial structure coordination degree are proposed in this paper. The weighted average values of the industrial structures of the developed countries such as the USA, UK, France, Germany and Japan in the year when their per capita GDP reaches US $10000, US $20000 and US $30000 was calculated respectively. Then, the standard industrial structure was calculated with the weighted average values of the industrial structures of the five developed countries as reference frame. And the definition of industrial structure deviation and industrial structure coordination degree is put forward accordingly. At last, the coordination degree of industrial structure in China, Jiangsu Province, Southern Jiangsu, Central Jiangsu and Northern Jiangsu is calculated according to the data of actual industrial structure in 2019. And the industrial structure optimization and adjustment plan of China and Jiangsu Province is worked out according to the set target structure

Positive and Inverse Degree of Grey Incidence Estimation Model of Soil Organic Matter Based on Hyper-spectral Data

Hao Zhong, Li Li, Xican Li , Haoran Zhai , Xuesong Cao

2021, 33(2):  39-57. 

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To improve the estimation accuracy of soil organic matter based on hyper-spectral data when using degree of grey incidence, this paper first proposes the concept of the positive and inverse degree of grey incidence considering the limitation of the degree of grey incidence for estimating problems. Then, two new models of positive and inverse degrees of grey incidence are established. Thereafter, the properties of positive and inverse degree of grey incidence are analyzed. Moreover, the estimation model of soil organic matter using positive and inverse degree of grey incidence is established based on hyper-spectral data, and detailed calculation steps are given. Finally, the validity of the model is verified by taking 76 soil samples collected from Zhangqiu District, Jinan City, Shandong Province. The application results show that using the positive and inverse degree of grey incidence, the hyper-spectral estimation model of soil organic matter has high precision. The mean relative error (MRE) of 16 samples to be estimated is 5.312% and the determination coefficient (R2) is 0.930. The research shows that the positive and inverse degree of grey incidence proposed in this paper effectively expands the application of the degree of grey incidence. It is feasible and effective for hyper-spectral estimation of soil organic matter content, and meanwhile, it provides a new way to solve uncertain prediction problems.

Research on Grey Forecasting of The Peak and Its Application

Jinluan Yang , Yang Li, Chaoqing Yuan

2021, 33(2):  58-73. 

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GM(1,1) is mainly used for monotone sequence prediction. But when used for sequences with peaks, such as the total population, total energy consumption, etc, GM(1,1) is not a good consideration. This paper proposes a peak prediction algorithm based on the model GM(1,1) (GPPA) to forecast the development trend of the system in the long run. According to the developing law of the system, the growth inflection point is analyzed and determined, and from which the growth rate of the system will be smaller and its trend can be decomposed into two parts based on the logistic curve of population growth: one is the original growth trend and the other one is the restraining trend of the retarding factors. Metabolic GM(1,1) is used to model both the two trends respectively and get the predictions of them. The difference between the predictions is used to forecast the development of the system. In addition, the energy consumption and population are taken as examples to prove the applicability of the method.

The Economic Growth Effect of the Blue Economic Zone Based on a GRAM-DID Model

Rui Han, Kedong Yin , Xuemei Li

2021, 33(2):  74-94. 

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The marine economy is a new economic growth target under the new paradigms of China's economy. Since 2011, China has established successive marine economy demonstration zones. However, there is no conclusion yet drawn as to the effect of the marine economy demonstration zones. In this paper, the earliest Shandong Peninsula Blue Economic Zone is set as our research object, the GRAM-DID model is used to evaluate the net effect of the establishment of the Blue Economic Zone, and the marine economy of Shandong Province input-output model is compiled to estimate the economic growth effects of other industries induced thereby, which makes the assessment of the net effect more accurate and comprehensive. The results show that the net economic growth arising from establishment of the Blue Economic Zone is 304204.7 million yuan, which induces significant economic growth in the circulation of goods and services and the manufacturing sector.

Grain Yield Prediction Based on the Metabolic Grey - Markov Integration Model

Chao Fan, Fangfang Chen, Hao Lin, Litao Yang, Ashley Ndlovu

2021, 33(2):  95-108. 

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forecasting model based on the weighted Markov method is proposed. Since the grain yield is affected by many uncertain factors, the grain yield is predicted by the error amended metabolism grey model. Later, considering that the grain yield is also affected by the outputs over the years, the state transition probability matrixes are calculated, and the yield influence weights of the past years are decided. Lastly, combining the predicted yield and the influence weights, the final yield is corrected by recent years' yields, and the metabolic grey model is constructed. By using above procedures, the yields of 2016 to 2020 are predicted based on the data of 2005-2015, the results show that the forecasting error is less than 2% for all predicted years, and the mean error for 5 years achieves to 1.04%, which can be used to predict the grain yield accurately in the medium and short term.

A Graph model for Conflict Resolution based on a Grey Multi-criteria Preference Ranking Approach

Jian Li , Wanming Chen , Huanhuan Zhao , Renshi Zhang

2021, 33(2):  109-127. 

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Preference ranking is a vital issue in the process of graph model for conflict resolution (GMCR). Concerning the ranking problem of uncertainty preference, we propose a grey multi-criteria preference ranking approach based on grey interval numbers to represent the uncertainty preference of decision-makers, and calculate the comprehensive grey incidence degrees between DMs. And then, based on maximum entropy, we construct a multi-objective optimization model to minimize uncertainty. Finally, we exploit a real-world conflict incident of "Taihu Lake water pollution" to illustrate the feasibility and effectiveness of the proposed model

Analysis of Policy Effects on New-energy Vehicles

Zhen Chen, Kanghui Zhang∗, Shuwei Jia, Dongyong Zhang

2021, 33(2):  128-149. 

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This study used system dynamics to draw a stock-and-flow diagram for new-energy vehicles. A new approach called a "reverse even grey model (1,1)" was developed, and SPSS was used to process the data. Furthermore, the synthetic degree of incidence was used to examine the feasibility of the model. Four hypothetical scenarios for new-energy vehicles development were analyzed to find the ideal solution for China. These analyses showed the following: (1) Cadmium has certain environmental risks. At present, battery recycling is the main problem related to new-energy vehicles in China. (2) Total energy consumption is influenced by the dominant vehicle type in the market (new-energy vehicles and traditional fuel vehicles). (3) The government's purchase policy can promote sales of new-energy vehicles, but its effect is limited. Finally, (4) a "green paradox" effect exists in new-energy vehicles in China. Based on the findings, suggestions are made for the reasonable development of new-energy vehicles in China. Hence, this research has certain health and environmental benefits.

The Optimal Solution for Grey Fuzzy Flexible Linear Programming Problems Based on The Feasibility and Efficiency Concepts 

M. Asghari , Bing Yuan Cao , S.H. Nasseri

2021, 33(2):  150-165. 

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The purpose of this paper is to extend the newly established α- feasibility and α- efficiently concept for grey flexible fuzzy linear programming, so as to present some important new concepts, models, methods, and a new framework of grey system theory in mathematical programming. In this paper, we concentrate on Grey Fuzzy Flexible Linear Programming (GFFLP) problems as a reasonable extension of GLP models that adapt more to real situations. For this aim, after defining the classical GFFLP model, we first introduce a new concept of α ̅-feasibility and α ̅- efficiency to these problems, and then we propose a two-phase approach to solve the mentioned problems. Furthermore, we give some fundamental theorems and constructive results to support and verify the proposed solving process. This approach will be open a new window to the modeling of the problems in the real world under flexibility conditions. A lot of successful practical applications of the new models to solve various problems have been found in many different areas and disciplines such as agriculture, decision sciences, diet problem, ecology, economy, geology, earthquake, industry, material science sports, medicine, management, transportation, and etc. Because of the ability to deal with poor, incomplete, or uncertain problems with grey systems, most real-world processes in decision problems are in the grey stage due to lack of information and uncertainty. However, the flexibility assumption in decision making is more comfortable for the Decision Maker (DM), hence in this paper, we concentrate on Grey Fuzzy Flexible Linear Programming (GFFLP) problems as a reasonable extension of GLP models in which is more adept with the real situations, etc. These practical applications have brought forward definite and noticeable social and economic benefits. It demonstrates a wide range of applicability of grey system theory, especially when the available information is incomplete and the collected data is inaccurate. In this study, a general picture of grey mathematical programming under flexibility conditions is given as a new model and a new framework for various real problems where partial information is known; especially for uncertain decision systems with few data points and poor information.