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.

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

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.

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 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.

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

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 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.