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The purpose of this study is to construct a gray relational model based on information diffusion to avoid rank reversal when the available decision information is insufficient, or the decision objects vary.

Considering that the sample dependence of the ideal sequence selection in gray relational decision-making is based on case sampling, which causes the phenomenon of rank reversal, this study designs an ideal point diffusion method based on the development trend and distribution skewness of the sample information. In this method, a gray relational model for sample classification is constructed using a virtual-ideal sequence. Subsequently, an optimization model is established to obtain the criteria weights and classification radius values that minimize the deviation between the comprehensive relational degree of the classification object and the critical value.

The rank-reversal problem in gray relational models could drive decision-makers away from using this method. The results of this study demonstrate that the proposed gray relational model based on information diffusion and virtual-ideal sequencing can effectively avoid rank reversal. The method is applied to classify 31 brownfield redevelopment projects based on available interval gray information. The case analysis verifies the rationality and feasibility of the model.

This study proposes a robust method for ideal point choice when the decision information is limited or dynamic. This method can reduce the influence of ideal sequence changes in gray relational models on decision-making results considerably better than other approaches.

The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers.

First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes.

The effectiveness of the model is proved by an example of carrier aircraft selection.

The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields.

In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.

Advanced manufacturing system (AMS) offers opportunities for industries to improve their technology, flexibility and profitability through a highly efficient and focused approach to manufacturing effectiveness. Selecting a proper AMS is a complicated task for the managers as it involves large tangible and intangible selection attributes. Failure to take right decision in selecting proper AMS alternative may even lead industry to losses. The purpose of this paper, therefore, is to rank the AMS alternatives by using fuzzy grey relational analysis, which will help managers when choosing an appropriate AMS.

This research proposes a multi‐attribute decision‐making (MADM) method, fuzzy grey relational analysis (FGRA), for AMS selection. The methodology is explained as follows. AMS alternatives and selection attributes will be chosen. The qualitative attributes will be converted into quantitative using fuzzy conversion scale. Then these data will be pre‐processed to normalize every value. This step is done to convert all alternatives into a comparability sequence. According to these sequences a reference sequence (ideal target sequence) is defined. Then, the grey relational coefficient between all comparability sequences and the reference sequence is calculated. Finally, based on these grey relational coefficients, the grey relational grade between the reference sequence and every comparability sequences is calculated. If a comparability sequence translated from an alternative has the highest grey relational grade between the reference sequence and itself, then that alternative will be the best choice. Fuzzy logic is used here to convert linguistic data into crisp score.

The proposed method is validated and compared by taking two examples from literature. The traditional statistical techniques require large data sets for evaluating attributes while grey theory on the contrary solve the multi attribute decision making problems with small data sets. This methodology will significantly increase the efficiency of decision making and overall competitiveness for manufacturing industries. This approach will motivate more and more industries to invest in AMS.

This method will help managers to weigh the AMS alternatives before actually buying them, which will in turn save money and time. This will build confidence of the top management for investing in costly technology such as AMS.

From time to time, various researchers have proposed various techniques to select the AMS. However, a survey on current evaluation methods shows that they are all less objective, lack accurate data processing, involve large calculations because of their complexity. In this paper, the authors attempt to solve the problem of AMS selection with FGRA, which is more logical, axiomatic, generates results in fewer steps with less calculations and is easy to understand. This paper succeeds in getting AMS alternatives' ranking using fuzzy grey relational analysis.

The purpose of this paper is twofold: the selection of project portfolios through hybrid artificial neural network algorithms, feature selection based on grey relational analysis, decision tree and regression; and the identification of the features affecting project portfolio selection using the artificial neural network algorithm, decision tree and regression. The authors also aim to classify the available options using the decision tree algorithm.

In order to achieve the research goals, a project-oriented organization was selected and studied. In all, 49 project management indicators were chosen from A Guide to the Project Management Body of Knowledge (PMBOK Guide), and the most important indicators were identified using a feature selection algorithm and decision tree. After the extraction of rules, decision rule-based multi-criteria decision making matrices were produced. Each matrix was ranked through grey relational analysis, similarity to ideal solution method and multi-criteria optimization. Finally, a model for choosing the best ranking method was designed and implemented using the genetic algorithm. To analyze the responses, stability of the classes was investigated.

The results showed that projects ranked based on neural network weights by the grey relational analysis method prove to be better options for the selection of a project portfolio. The process of identification of the features affecting project portfolio selection resulted in the following factors: scope management, project charter, project management plan, stakeholders and risk.

This study presents the most effective features affecting project portfolio selection which is highly impressive in organizational decision making and must be considered seriously. Deploying sensitivity analysis, which is an innovation in such studies, played a constructive role in examining the accuracy and reliability of the proposed models, and it can be firmly argued that the results have had an important role in validating the findings of this study.

The purpose of this paper is to evaluate, analyse and select the best suppliers for Satrec Vitalait Milk Company, operating in Senegal, based on criteria obtained from economic, environmental and social dimensions of sustainable supply chain management, through the application of Deng’s grey relational analysis (GRA) model, absolute GRA model (ADGRA) and a novel second synthetic GRA (SSGRA) model, combined with one decision making under the uncertainty-based model, namely, the Hurwicz criteria.

The research adopts a new synthetic GRA model and highlights its reliability on small sample gathered from four senior experts of the company who administered a total number of 28 specialists operating in four departments of the company, through the employment of a self-administered questionnaire designed based on criteria identified from the literature that were refined via a Q-sort model.

The outcomes of the research methodology designated that all the selected five suppliers present a degree of attaining sustainability due to the fact that supplying unprocessed milk does not require the use of polluting methods for stocking and transportation. The undertaken study specifies that all the socio-environmental criteria play a crucial role in shaping the sustainability level of Satrec Vitalait’s suppliers and demonstrates the accuracy of the results obtained through the second synthetic degree of grey relation analysis for ranking the suppliers. Supplier 2 was found to be the best supplier for the company and, as result, a model for other suppliers to mimic.

Future researchers can replicate the GRA-based supply chain model proposed in the current study in different environments especially in the context of green supply chain. Also, in future the SSGRA model, while using the bidirectional ADGRA instead of the conventional ADGRA, should also be tested, especially when the data sequences associated with different supply chain parameters have inconsistent directions. Also, comparative analysis of SSGRA-based results with that of modern statistical methods like structural equation modelling can also be used for future explorations. Furthermore, the current study is built upon the data associated with the Satrec Vitalait Milk Company (Senegal); therefore, the findings should be generalised with caution.

The study can be seen as a first-stepping stone for gauging and selecting the best sustainable supplier for Satrec Vitalait using grey system theory. For purpose of attaining the research goal, the SSGRA was exploited as an innovative experimental approach to estimate relationships between criteria with regard to the sustainability level of the company’s suppliers. Under this scope, relationships between criteria themselves and their goal were depicted by Deng’s degree of GRA and AGRA, respectively. The research is innovative by means of the framework of its methodology and data analysis.

Due to the limitation of acknowledgment, the complexity of software system and the interference of noises, this paper aims to solve the traditional problem: traditional software cost estimation methods face the challenge of poor and uncertain inputs.

Under such circumstances, different cost estimation methods vary greatly on estimation accuracy and effectiveness. Therefore, it is crucial to perform evaluation and selection on estimation methods against a poor information database. This paper presents a grey rough set model by introducing grey system theory into rough set based analysis, aiming for a better choice of software cost estimation method on accuracy and effectiveness.

The results are very encouraging in the sense of comparison among four machine learning techniques and thus indicate it an effective approach to evaluate software cost estimation method where insufficient information is provided.

Based on the grey rough set model, the decision targets can be classified approximately. Furthermore, the grey of information and the limitation of cognition can be overcome during the use of the grey rough interval correlation cluster method.

This paper proposed the grey rough set model combining grey system theory with rough set for software cost estimation method evaluation and selection.

This paper aims to deal with the grey relational problem of panel data with an attribute value of interval numbers. The grey relational model of interval number for panel data is constructed in this paper.

First, three kinds of interval grey relational operators for the behavior sequence of a dimensionless system are proposed. At the same time, the positive treatment method of interval numbers for cost-type and moderate-type indicators is put forward. On this basis, the correlation between the three-dimensional interval numbers of panel data is converted into the correlation between the two-dimensional interval numbers in time series and cross-sectional dimensions. The grey correlation coefficients of each scheme and the ideal scheme matrix are calculated in the two dimensions, respectively. Finally, the correlation degree of panel interval number and scheme ordering are obtained by arithmetic mean.

This paper proves that the grey relational model of the panel interval number still has the properties of normalization, uniqueness and proximity. It also avoids the problem that the results are not unique due to the different orders of objects in the panel data.

The effectiveness and practicability of the model is verified by taking supplier selection as an example. In fact, this model can also be widely used in agriculture, industry, society and other fields.

The accuracy of the relational results is higher and more accurate compared with the previous studies.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

The purpose of this paper is to seek an approach to study decision making and optimization analyzing of enterprises with multi‐factors.

In this paper, a new grey decision dynamic model was set up; it integrates with modified GM model, the transfer function and response characteristic of cybernetics, and other knowledge. The building steps of this integrated model and its application method in a certain enterprise were presented.

Until recently, there have been many references studying grey decision or grey relational analysis of factors, but it was found that dynamic affecting of multi‐factors for enterprise production and their affecting levels were not studied synthetically in these references, and by this new dynamic model, these useful conclusions can be gotten.

The built time response equation and dynamic model in this paper can be only used for whole regularity analysis and not suited to daily one‐to‐one analyzing; otherwise the error of the reductive values must be tested.

This new grey decision dynamic model can be used widely in decision making and optimization analyzing of enterprises with multi‐factors. Practical applying results show that the proposed method can instruct effectively actual production.

This paper offers a new grey decision dynamic model that can be used in decision making and optimization analyzing of enterprises with multi‐factors. By applying this new dynamic model in practice, some useful conclusions are drawn; some dynamic factors affecting production capacity of enterprises and their affecting levels can be found.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.