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With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

Tour social network data that are heterogeneous contain not only the quantitative structured evaluation data, but also the qualitative non-structured data. This is a big data scenario. How to evaluate tour online review and then recommend to potential tourists quickly and accurately are important parts of social responsibility of tour companies. The purpose of this paper is to propose a new method for evaluating tour online review based on grey 2-tuple linguistic.

The phenomenon of “poor information” exists in some big data scenario. According to social responsibility, grey 2-tuple linguistic evaluation model for tour online review is proposed.

Tour social networks contain data that are valuable to each individual on tourism industry's value chain. Grey 2-tuple linguistic evaluation model can be used for evaluating tour online reviews. This is a systems thinking method that takes social responsibility into account.

Due to the complex links among reviewers in social network, network mining approaches and models are expected to be added to this research in the near future.

Grey 2-tuple linguistic evaluation method can contribute to the future research on evaluating a variety of tour social network comment data in the real world.

A new evaluation method for making evaluation and recommendations based on tour social network comment information is proposed.

This paper attempts to put forward a convincing and flexible grey cluster method that can be used in confirming credibility level of trustworthy software development process.

Determination on credibility level in the software development process is dynamic, as credibility of the results may be different at different times and under different project requirements. Qualitative methods are not entirely convincing, and most quantitative methods are not flexible enough. Grey cluster based on nonlinear grey whitening weight function put forward in this paper is both convincing and flexible. Finally eight projects from the ISBSG database are used for empirical analysis, which confirm that the method put forward in this paper is available and credible.

The results are convincing: not only that grey cluster based on nonlinear grey whitening weight function put forward in this paper is both convincing and flexible, but it can be used in confirming credibility level of trustworthy software development process.

Eight projects from the ISBSG database are used for empirical analysis, which confirms that the method put forward in this paper is available and credible.

Nonlinear grey whitening weight function is derivable except endpoint. Grey cluster based on nonlinear grey whitening weight function put forward in this paper is both convincing and flexible.

The purpose of this paper is to attempt to realize a distribution network optimization in supply chain using grey systems theory for uncertain information.

There is much uncertain information in the distribution network optimization of supply chain, including fuzzy information, stochastic information and grey information, etc. Fuzzy information and stochastic information have been studied in supply chain, however grey information of the supply chain has not been covered. In the distribution problem of supply chain, grey demands are taken into account. Then, a mathematics model with grey demands has been constructed, and it can be transformed into a grey chance‐constrained programming model, grey simulation and a proposed hybrid particle swarm optimization are combined to resolve it. An example is also computed in the last part of the paper.

The results are convincing: not only that grey system theory can be used to deal with grey uncertain information about distribution of supply chain, but grey chance‐constrained programming, grey simulation and particle swarm optimization can be combined to resolve the grey model.

The method exposed in the paper can be used to deal with distribution problems with grey information in the supply chain, and network optimization results with a grey uncertain factor could be helpful for supply chain efficiency and practicability.

The paper succeeds in realising both a constructed model of the distribution of supply chain with grey demands and a solution algorithm of the grey mathematics model by using one of the newest developed theories: grey systems theory.

No matter in product design stage or product quality improvement stage, the product technical requirements (PTRs) impacting on product performance are mainly identified and improved to optimize customer needs (CNs) as greatly as possible. The purpose of this paper is to use a new approach to accurately and properly prioritize PTRs in Quality Function Deployment (QFD).

Considering the relationship between CNs and PTRs in QFD hard to quantitatively measure and lack of adequate information, the grey relational analysis (GRA) is applied. Besides, owing to the inherent inner dependencies of CNs as well as QFD, the analytic network process (ANP) which is an effective tool to take into account the inner dependencies with network is utilized. Finally, an integrated framework based on GRA method and ANP approach is proposed to determine the importance degrees of PTRs in designing a product.

The calculation results of the application show that the proposed framework by integrating GRA and ANP is able to determine the importance degrees of PTRs in design a product well.

In the application part the authors can see the proposed framework can be applied to PVC window systems. Owing to that traditional QFD method has been widely used in many fields such as manufacturing industry and service industry, actually the proposed framework has great potential application in the real word.

This paper succeeds in proposing a new framework by integrating GRA and ANP to determine the importance degrees of PTRs in QFD.

Since the present university leading cadre capability evaluations are mainly static, there is a lack of evaluation methods to address dynamic and multi‐scale‐based situations. Some important factors to address people's performances, such as the developing trends and potentialities are considered. The purpose of this paper is to attempt to propose a dynamic evaluation model based on multivariate delay connection number.

First, evaluation results of previous periods are taken into the current period and values of uncertain numbers are revised. Next, a dynamic evaluation model is built for university leading cadre. Last, a numerical example is used to prove its feasibility and effectiveness.

This paper promotes a corrected equal portion value method of uncertain numbers i1(τ), … ,in(τ) from the adjacent two periods to multi periods, and builds a dynamic evaluation model, which proved simple‐structured and easy to apply.

The new model is applied in the evaluation of university leading cadre, and could give a more comprehensive, dynamic, scientific analysis for evaluation by model application.

The paper develops a novel evaluation model based on a multivariate delay connection number, which expands traditional methods from a static situation to a dynamic and multi‐scale‐based situation.

The purpose of this paper is to attempt to solve a decision‐making problem for breeding target character showed as an interval number.

A new comprehensive evaluation method was proposed based on similarity‐difference theory and interval number theory. The data from Winter Wheat Group I Variety Regional Test in Henan Province in 2009‐2010 were analysed using the proposed method.

The results showed that Zhou 99233 was a good variety, Yuxhan No.7, An 05‐28, Xun K8, Jinyumai 378 and Zhoumai 18 were better ones, 08 luo 36, and Xuke 718 ordinary ones, and others worse ones. Based on this, the feasibility of the method was discussed. It showed that the proposed method had some obvious merits, such as arithmetic was simple, operation convenient, flexible and practical, fast and effective, etc.

Application to a living example indicated that its evaluation effect was satisfactory. Consequently, the application prospect of the method will be very vast.

The paper succeeds in solving a decision‐making problem for breeding target character shown as an interval number.

The purpose of this paper is to establish a combined forecasting model to predict regional logistics demand, which is an important procedure on decision making of regional logistics planning.

There are several kinds of mathematical models often used in forecasting regional logistics demand. Trend extrapolation method extrapolates the future development trends bases on the hypothesis that the regional logistics will develop steadily. Grey system method predicts the change of logistics demand by the generation and development of original data sequence and excavation of inherent rules of the original data. Regression method obtains the change rules through the analysis between explained variable and explanatory variables. Each method has unique characteristics. In order to improve the accuracy of the prediction, combined methods are established. Genetic algorithm is used to determine the weights of different single models.

The results show that the combined forecasting model optimised by genetic algorithm can improve the accuracy.

Combined forecasting model can integrate the advantages of different single forecasting models. The key of improving the accuracy is to determine the weights of single forecasting models. Genetic algorithm can do well in finding suitable weights of each single forecasting model.

The paper succeeds in providing a combined forecasting model using genetic algorithm to determine the weights of each single prediction model, which helps to the decision making of regional logistics demand.