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Grey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.

Firstly, the bull's eye matrix of the scheme stage is derived based on the principle of group negotiation and maximum satisfaction deviation. Secondly, a nonlinear programming model for stage weight is constructed by using an improved Orness measure constraint to determine the stage weight. Finally, the quantum-like Bayesian network is constructed to explore the interference effect between stages. In this process, the decision of each stage is regarded as a wave function which occurs synchronously, with mutual interference impacting the aggregate result. Finally, the effectiveness and rationality of the model are verified through a public health emergency.

The research shows that there are interference effects between each stage. Both the dynamic grey target group decision model and the dynamic multi-attribute group decision model based on quantum-like Bayesian network proposed in this paper are scientific and effective. They enhance the flexibility and stability of actual decision-making and provide significant practical value.

To address issues like stage interference effects, subjective bull's eye settings and the absence of participative behavior in decision-making groups, this paper develops a grey target decision model grounded in group negotiation and maximum satisfaction deviation. Furthermore, by integrating the quantum-like Bayesian network model, this paper offers a novel perspective for addressing information fusion and subjective cognitive biases during decision-making.

Based on the characteristics of interval number, the distance of interval number is defined. And based on the grey incidence degree theory, the degree of interval number incidence is defined. These extend grey incidence analysis theory from real number sequence to interval number sequence.

Studies the multi‐attribute incidence decision‐making problems for interval number and models the incidence decision‐making model of multi‐attribute interval number.

An application example is given based on grey incidence decision model with multi‐attribute interval number.

This new model can avoid the difficulty of seeking the dummy optimal scheme and the negative optimal scheme, and it regards evaluated scheme as a whole to seek the optimal scheme.

It is easy to realizing on computer and the evaluated result is more objective than the results obtained by other methods.

Studies multi‐attribute decision‐making problems.

The purpose of this paper is to propose a dynamic multi-attribute decision-making method based on the prospect theory for dealing with the dynamic multi-attribute decision-making problem with three-parameter interval grey number.

First, the kernel and comparison rule of three-parameter interval grey numbers are defined, which are the basis of collecting and sorting grey numbers. Next, the prospect value function is determined in view of the decision-making information with different time points as the reference points. Then, an optimal model for solving the attribute weight and time weight is constructed based on the grey entropy principle.

The paper provides a dynamic grey interrelation decision method based on the prospect theory with three-parameter interval grey number, and the example analysis shows that the method proposed in this paper has validity and rationality.

If we have a better understanding of the weights of different reference points, it is possible to receive a more reasonable expression for the comprehensive prospect utility value function.

The paper provides a grey interrelation decision method based on the prospect theory, which can help the decision maker deal with the dynamic multi-attribute decision-making problems under the uncertain environment.

The paper proposes the kernel and ranking method of three-parameter interval grey number, and uses different time points as the reference points to define the prospect value function. Furthermore, this paper structures a dynamic grey interrelation decision method with three-parameter interval grey number based on the prospect theory.

The purpose of this paper is to investigate the range sensitivity of the analytic hierarchy process (AHP) and evaluate the effectiveness of using a bottom‐up approach to mitigate the possible range insensitivity bias in the AHP.

An experiment was conducted to test the normative range‐sensitivity of four different methods: the AHP with bottom‐up evaluation; direct ratio weights; swing weights; and trade‐off weights. Also, the significance of the range‐sensitivity effects and the differences among weighting approaches were rigorously tested using various statistical models.

Results show that the range sensitivities of AHP and direct ratio weights are significantly less than the range sensitivities of swing weights and tradeoff weights, suggesting that the bottom‐up evaluation approach might not be a feasible solution for the range‐insensitivity problem. This finding is consistent with the value‐comparison hypothesis proposed in an earlier study, and is partially supported by the theory of the multi‐dimensionality of attribute importance.

It is concluded that treating the attribute weights and performance scoring scales separately in the AHP or other multi‐attribute decision analysis models might lead to an arbitrary final ranking of alternatives. Therefore, it may be necessary to incorporate better elicitation procedures into the AHP models to ensure that attribute weights properly reflect the range or scale of measurement.

This study provides new evidence and issues words of warning of the range‐sensitivity effects in the multi‐attribute decision analysis.

The paper aims to research the multi-attribute decision-making in which the attribute values are interval grey numbers and the maximum probability of the value of grey number is known.

First, the authors define deviation degree and dominance relation of three-parameter interval grey number and get the equivalence between the dominance relation of decision-making object and the sum of three values of three-parameter interval grey number. Then, considering the uncertainty of goal weight, the authors construct multi-index optimization model based on deviation degree and get the goal weight.

The authors prove the rationality and effectiveness of decision-making methods by examples and give a new thought for grey multi-attribute decision-making methods.

As a paper research on theory, it offered a new multi-attribute decision-making method with three-parameter interval grey number.

To develop a multi‐attribute decision making model for evaluating and selecting among logistic information technologies.

First a multi‐attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented.

Reviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone for future business; policy issues as risk and necessity level; resources as costs and completion time. Presents a methodology that is developed for the complex, uncertain and vague characteristics of the problem.

Comparisons with other multi‐attribute decision making techniques such as AHP, ELECTRE, PROMETHEE and ORESTE under fuzzy conditions can be done for further research.

This article is a very useful source of information both for logistic managers and stakeholders in making decisions about logistic information technology investments.

This paper addresses the logistic information technology evaluation and selection criteria for practitioners and proposes a new multi‐attribute decision making methodology, hierarchical fuzzy TOPSIS, for the problem.

The purpose of this paper is to propose the grey target decision method based on three-parameter interval grey number for dealing with multi-attribute decision-making problems under uncertain environment.

First, the kernel and ranking method of three-parameter interval grey number are defined, which is the basis of determining the positive and negative bull’s-eye. Next, a new distance measure of three-parameter interval grey number is defined in view of the importance of the “center of gravity” point. Furthermore, a new comprehensive bull’s-eye distance is proposed based on the kernel which integrates the distance between different attributes to the positive and negative bull’s-eye. Then attribute weights are obtained by comprehensive bull’s-eye distance minimum and grey entropy maximization.

The paper provides a grey target decision method based on three-parameter interval grey number and example analysis shows that the method proposed in this paper is more reasonable and effective.

If we have a better understanding of the distribution characteristics of three-parameter interval grey number, it is possible to have a more reasonable measure of the distance of three-parameter interval grey number.

The paper provides a grey target decision method, which can help decision maker deal with multi-attribute decision-making problems under uncertain environment.

This paper proposed the kernel and ranking method of three-parameter interval grey number, and defined a new distance measure of three-parameter interval grey number and proposed a new comprehensive bull’s-eye distance, Furthermore, this paper structured a grey target decision method based on three-parameter interval grey number.

We formulate a multi‐attribute decision model for preventive replacement of a “magnetic sealing head” in a soft‐drink producing factory in the Kingdom of Saudi Arabia. In case of failure of this part, the opportunity cost (for production losses) is very important, as the entire production line will be idle. We determine in a first case the replacement policy that minimizes the total expected unit cost of replacement (preventive and corrective). Next, we determine the optimal policy that maximizes the expected multi‐attribute utility of the decision‐maker in the factory. Four attributes are considered in the replacement problem, namely cost, quality, labor productivity, and cash flow availability. The optimal policy in each case outperforms by far the one applied in the plant, which turns out to be costly and inefficient with respect to the utility of the decision‐maker.

Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.

First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.

To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.

The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.

Safety data sheets are documents developed by chemical manufacturers to identify and label hazardous materials. The occupational safety and health administration regulations state that employers must make safety data sheets available for employees. When firms use hundreds of chemicals, tracking their safety data sheets becomes difficult. Safety Data Sheet Management Systems are developed to track safety data sheets. This paper aims to propose a multi-attribute decision-making framework for selecting a Safety Data Sheet Management System.

A total of 12 attributes are proposed based on a real-life project conducted at a firm in New York and the software selection models existed in the literature. Fuzzy technique for order of preference by similarity to ideal solution is used to assess the proposed attributes and alternatives. A case study and sensitivity analysis are conducted to show the robustness of the proposed model. Fuzzy analytical hierarchy process is used for validation.

Safety Data Sheet Management System is important for firms to track and manage safety data sheets. The proposed framework is practical and easy to implement.

The proposed decision model is useful for firms to select a proper Safety Data Sheet Management System. The system developers can use the model to update their systems.

This paper develops a new multi-attribute decision-making model for selecting a Safety Data Sheet Management System. To the best of the authors’ knowledge, no previous study has developed such a model.