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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.

Decision-making problems in emergency plan selection for epidemic prevention and control (EPAC) are generally characterized by risky and uncertainty due to multiple possible emergency states and vagueness of decision information. In the process of emergency plan selection for EPAC, it is necessary to consider several obvious features, i.e. different states of epidemics, dynamic evolvement process of epidemics and decision-makers' (DMs') psychological factors such as risk preference and loss aversion.

In this paper, a novel decision-making method based on cumulative prospect theory (CPT) is proposed to solve emergency plan selection of an epidemic problem, which is generally regarded as hybrid-information multi-attribute decision-making (HI-MADM) problems in major epidemics. Initially, considering the psychological factors of DMs, the expectations of DMs are chosen as reference points to normalize the expectation vectors and decision information with three different formats. Subsequently, the matrix of gains and losses is established according to the reference points. Furthermore, the prospect value of each alternative is obtained and the comprehensive prospect values of alternatives under different states are calculated. Accordingly, the ranking of alternatives can be obtained.

The validity and robustness of the proposed method are demonstrated by a case calculation of emergency plan selection. Meanwhile, sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and TODIM (an acronym in Portuguese for interactive and MADM) method illustrate the effectiveness and superiority of the proposed method.

An emergency plan selection method is proposed for EPAC based on CPT, taking into account the psychological factors of DMs.

Highlights

1. This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

2. The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

3. This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

4. The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

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.

The paper attempts to construct a novel multi-objective grey hierarchical group consensus approach to deal with the group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among decision makers (DMs).

To deal with these group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among DMs non-cooperative behavior in uncertain information systems, considering the influence of coordination cost and the degree of group consensus, based on the idea of grey situation decision-making, the authors establish a multi-objective grey hierarchical group consensus model, and design different invalid decision elimination rules for decision-making groups of different sizes, and use a case verifies the effectiveness and feasibility of the model.

With the continuous improvement of the coordination cost budget, the degree of consensus of all departments and the overall consensus tend to be stable, and will no longer change with the increase of the coordination cost budget. The cost required by each department is basically consistent with the response trend of the cost required to coordinate the overall situation to the pre-set lower limit of group consensus.

The proposed approach can succeed in identifying DMs' information, and mine the DMs' information and help make a relatively more scientific decision.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

With the rapid advancement of technology, companies use new technologies to produce their products and services to maintain a competitive advantage. As companies alone cannot research and develop their technologies, they should use knowledge sources outside the organization that may exist throughout the world; hence, organizations need technology transfer. Because the success rate of technology transfer projects is low, the need to accurately assess and investigate the critical success factors of technology transfer projects is felt. In this regard, this study aims to identify and prioritize the critical success factors in technology transfer projects.

In this research, 56 critical success factor (CSF) were extracted from the context of the articles and were adjusted using experts’ opinions in different phases, as well as the fuzzy-Delphi approach. Finally, 15 factors were categorized in the form of steps of the technology transfer model: STAGE-GATE. In the next step, the set of criteria needed to prioritize CFSs was extracted from the literature and finalized with the help of the experts. Then, how each of the CSF influences the identified criteria was scored according to the organization’s export opinions. Finally, the priority of each key success factor was calculated using the additive ratio assessment (ARAS) method.

The results obtained for prioritization of the critical success factors show that experience in technology transfer in the transferee company, the existence of experienced technology transfer managers, sufficient organizational infrastructure and documenting project problems, achievements and experiences are four critical success factors of the technology transfer projects. Considering the long-term and short-term specific goals of the technology transfer process and the choice of technology in line with the company’s commercial strategy are also the critical success factors with the next priorities.

The combination of ARAS and step-wise weight assessment ratio analysis methods for identifying and prioritizing managerial decisions in the high-tech industries is a value of this research. Also, a combination of novel multi-attribute decision-making methods by the older framework of new product development is another contribution of this research.

As public–private partnerships (PPPs) have become preferred and veritable approach to deliver affordable housing, the seemingly lack of understanding of the significant factors that impact on success has become a notable setback. This study aims to delineate significant factors that can support decisions in affordable PPP public housing delivery.

Largely, a questionnaire survey was adopted to elicit insights from practitioners, policymakers and experts to develop an evaluative decision support model using an analytical hierarchy process and multi-attribute utility technique approach. Further, an expert illustration was conducted to evaluate and validate the results on the housing typologies.

The results revealed that energy efficiency and low-cost green building materials scored the highest weighting of all the criteria. Furthermore, multi-storey self-contained flats were found to be the most preferred housing typology and were significantly influenced by these factors. From the model evaluation, the scores on the factors of sustainability, affordability, cultural values and accountability were consistent across all typologies of housing whereas that of benchmarking, governance and transparency were varied.

The decision support factors captured varied dimensions of key factors that impact on affordable PPP housing that have not been considered in an integrated manner. These findings offer objective and systematic support to decision-making in affordable PPP housing delivery.

The purpose of this paper is to establish a two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem with three-parameter interval grey number to evaluation of agricultural drought resistance grade of 18 cities in Henan Province.

The clustering process is divided into two stages. In the first stage: Combining variance and time degree, the time weight optimization model is established. Applying the prospect theory, the index weight optimization model is established. Then, with the help of grey possibility function, the first stage of grey cloud clustering evaluation is carried out. In the second stage: the weight vector group of kernel clustering is constructed, and the grey class of the object is determined. A two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem is proposed.

This paper indicates that 18 cities in Henan Province are divided into four categories. The drought capacity in Henan province is high in the east and low in the west, high in the south and low in the north and the central region is relatively stable. The drought is greatly affected by natural factors. And the rationality and validity of this model is illustrated by comparing with other methods.

This paper provides a practical method for drought resistance assessment, and provides theoretical support for farmers to grasp the drought information timely and improve the drought resistance ability.

The model in this paper solves the situation of ambiguity and randomness to some extent with the help of grey cloud possibility function. Moreover, the time weight of time degree and variance are used to reduce the volatility and the degree of subjective empowerment. Considering the risk attitude of the decision makers, the prospect theory is applied to make the index weight more objective. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

The purpose of this article is to present the idea of a T-spherical hesitant fuzzy set associated with probability and to develop an extended multi-attributive border approximation area comparison (MABAC) method under probabilistic T-spherical hesitant fuzzy (Pt-SHF) settings.

The authors define some basic operational laws for Pt-SHF sets (Pt-SHFSs) and a comparison method of two probabilistic T-spherical hesitant fuzzy numbers (Pt-SHFNs) is proposed. Moreover, some Pt-SHF aggregation operators and the multi-attributive border approximation area comparison (MABAC) method are established under Pt-SHF scenario to solve group decision making problems.

The developed Pt-SHF MABAC method for multi-attribute group decision making (MAGDM) can overcome the drawbacks of conventional MABAC method and limitations for decision makers, which they face while providing their assessment concerning any object.

Clearly, this paper is devoted to MABAC method, MAGDM and probabilistic T-spherical hesitant fuzzy set theory.

The approach established can be used in a variety of scenarios, including decision making, engineering, and economics. An explanatory example is illustrated which shows the superiority and effectiveness of our proposed technique.

If a T-spherical fuzzy MAGDM problem under the probabilistic scenario needs to be evaluated, the involvement of probabilities in fuzzy system will be lost because of no information. This work fills a gap in literature by establishing the notion of probabilistic t-spherical hesitant fuzzy set to deal with the ambiguity, uncertainty in decision making problems.

The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.

First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.

The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.

This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.

A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.