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The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set.

To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model.

The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules.

The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers.

The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.

Looks at the marketing literature on the subject of multi‐attribute utility models. Reviews the multiple objective and multi‐dimensional preference models within the framework of multiple attribute utility theory. Suggests that much of the research in this area has been information‐ rather than decision‐oriented and, as a result, this has not been integrated successfully into the field of strategic marketing policy‐making.

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.

The purpose of this paper is to establish the index system of leading industries in Kashgar urban agglomeration, and use the multi-attribute weighted intelligent grey target decision-making evaluation model to measure the comprehensive effect, so as to select the leading industries of Kashgar urban agglomeration.

First, 18 industries in Kashgar urban agglomerations are taken as objectives, and four indexes, namely, demand income elasticity index, growth rate index, labor productivity growth rate index and contribution rate of output value, are selected to construct an evaluation system for leading industry selection in Kashgar urban agglomerations. Then, grey incidence degree method is used to determine the decision-making power of each decision-making objective. Finally, multi-attribute weighted intelligent grey target decision-making evaluation model is used to measure the comprehensive effect of the objective system of leading industries in Kashgar urban agglomerations.

It can be seen that the multi-attribute weighted intelligent grey target decision-making evaluation model is more convenient to be used in selecting regional leading industries, and the results are accurate and feasible. Based on the calculation results and the actual economic development requirements of Kashgar urban agglomeration, the leading industries of Kashgar urban agglomeration can be determined as: wood processing, furniture, paper making and printing; wholesale and retail; construction; equipment manufacturing; transportation, storage and postal services.

First, it is a new method in selecting regional leading industry by using the multi-attribute weighted intelligent grey target decision-making evaluation model. Second, since there is relatively little research on Kashgar urban agglomeration, especially on leading industries in Kashgar urban agglomeration. The research in this paper can not only enrich the research on selecting leading industries in urban agglomeration but also provide scientific reference for relevant government agencies to formulate economic development plans.

The garbage can model showed that what appears to be irrational and unpredictable choices can be explained by processes that regulate attention allocation and the availability of choice alternatives. Because attention to alternatives fluctuates, the model generates context-dependent choices: evaluations of alternatives depend on the mix of other alternatives considered. I re-examine the mechanisms by which fluctuating attention can cause context-dependent choices. Using insights from behavioral decision theory I demonstrate how adding fluctuating attention to a well-known model of organizational decision making generates context-dependent choices of a kind that could not be explained by a maximizing process.

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 reveal the importance of the order of establishment of temporary logistics hubs (TLHs) when resources (mobile storage units used as TLHs) are limited and to present the development and implementation of a methodology that determines the order of establishment of TLHs to support post-disaster decision making.

It employed a decision support system that considers multiple decision makers and subjective attributes, while also addressing the impreciseness inherent in post-disaster decision making for ordering the establishment of TLHs. To do so, an optimization model was combined with a fuzzy multi-attribute group decision making approach. A numerical illustration was performed using data from the April 2015 Nepal Earthquake.

The results showed the location and order of establishment of TLHs, and demonstrated the impact of decision makers’ opinions on the overall ordering.

The study does not discuss the uncertain nature of the location problem and the potential need for relocation of TLHs.

This methodology offers managerial insights for post-disaster decision making when resources are limited and their effective utilization is vital. The results highlight the importance of considering the opinions of multiple actors/decision makers to enable coordination and avoid complication between the growing numbers of humanitarian responders during disaster response.

This study introduces the concept of the order of establishment of TLHs and demonstrates its importance when resources are limited. It develops and implements a methodology determining the order of establishment of TLHs to support post-disaster decision making.

For the multi-stage and multi-attribute risk group decision-making problem, the attribute weight, decision-maker weight and time weight are unknown. The attribute value is grey information. The purpose of this paper is to discuss a decision-making method.

Analysis techniques and the theory about distance degree are used to determine the decision-maker weight within single stage. Grey relational analysis method is applied to determine the attribute weight. Moreover, the uncertainty of time weight and the proximity between the attribute value and positive/negative value are taken into account. A multi-objective optimization model is established based on maximum entropy to obtain time weights, so the comprehensive value is determined.

An example shows the effectiveness and practicability.

For a decision-making process, the results are different in different periods. This method is computationally very simple, easily comprehensible.