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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 extend the PROMETHEE method under typical hesitant fuzzy information for solving multi-attribute decision-making problem in which there is hesitancy among experts.

Different aggregation and distance functions were developed to deal with HFS. But it is rational that different operators applying in existing methods can produce different results. Also, it is difficult for decision makers to select suitable operators. To address the drawback, this paper develops the PROMETHEE method as an outranking approach to accommodate hesitant fuzzy information. Since the proposed method is constructed on the basis of the pair-wise comparisons, it is independent of the aggregation and distance functions.

To demonstrate the efficiency and accuracy of the proposed method, the authors provide a numerical example and a comparative analysis. The results indicate that outranking-based methods suggest a better ranking than the aggregation- and distance-based methods.

The proposed approach does not consider the hesitant fuzzy linguistic information decision-making problem.

The proposed approach can be applied in many group decision-making problems in which there is hesitancy among experts.

This paper proposes an extension on PROMETHEE method under hesitant fuzzy information, which has not been reported in the existing academic literature.

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.

In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects’ performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers.

In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of “criteria weights” and “projects” performance’ on evaluating projects in achieving the organizations’ goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes.

The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers.

Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.

This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives.

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.

One of the key success factors for an organization is the promotion of qualified personnel for vacant positions. Especially, the promotion of middle and senior managers play an important role in terms of organization’s success. In personnel promotion problem in which the candidates are nominated within the organization and they have been working for a specific period of time and are known in their organization, the candidates should be evaluated based on their recent as well as past performances to make right selection for the vacant position. For this reason, the purpose of this paper is to propose an integrated dynamic multi-attribute decision-making (MADM) model based on intuitionistic fuzzy set for solving personnel promotion problem.

The proposed model integrates analytic hierarchy process (AHP) technique and the dynamic evaluation by intuitionistic fuzzy operator for personnel promotion. AHP is employed to determine the weight of attributes based on decision maker’s opinions, and the dynamic operator is utilized to aggregate evaluations of candidates for different years. Atanassov’s intuitionistic fuzzy set theory is utilized to represent uncertainty and vagueness in MADM process.

A numerical example is presented to show the applicability of the proposed method for personnel promotion problem and a sensitivity analysis is conducted to demonstrate efficiency of dynamic evaluation. The findings indicate that the varying weights of years employed determined the best candidate for promotion.

The novelty of this study is defining personnel promotion as a MADM problem in the literature for the first time and proposing an integrated dynamic intuitionistic fuzzy MADM approach for the solution, in which the candidates are evaluated at different years.

The purpose of this paper is to provide a structured methodology to permit the optimal selection of the best‐suited computerized maintenance management system (CMMS) software within maintenance information technologies.

The analysis has been executed adopting a multi‐attribute decision‐making methodology, namely the technique for order preference by similarity to an ideal solution (TOPSIS). For the selection process, 17 criteria under five main heading have been defined. Data obtained from questionnaires and interviews with the company's maintenance managers have been used in fuzzy TOPSIS.

The application of the proposed approach allows the maintenance practitioners to concentrate on a limited subset of CMMS applications and to compare their actual capabilities in order to select the right one, rather than considering only their purchase cost.

Comparisons with other multi‐attribute decision‐making techniques such as AHP (analytic hierarchy process) and ELECTRE (elimination and choice expressing reality) under fuzzy conditions can be done for further research.

This paper is a very useful source of information both for maintenance managers and stakeholders in making decisions about the selection of CMMS software.

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

Quantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.

Each decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.

The approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.

The approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.

This approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.

This robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

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.