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In practical linguistic multi-criteria group decision-making (MCGDM) problems, words may indicate different meanings for various decision makers (DMs), and a high level of group consensus indicates that most of the group members are satisfied with the final solution. This study aims at developing a novel framework that considers the personalized individual semantics (PISs) and group consensus of DMs to tackle linguistic single-valued neutrosophic MCGDM problems. First, a novel discrimination measure for linguistic single-valued neutrosophic numbers (LSVNNs) is proposed, based on which a discrimination-based optimization model is built to assign personalized numerical scales (PNSs). Second, an extended consensus-based optimization model is constructed to identify the weights of DMs considering the group consensus. Then, the overall evaluations of all the alternatives are obtained based on the LSVNN aggregation operator to identify the ranking of alternatives. Finally, the results of the illustrative example, sensitivity and comparative analysis are presented to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the increasing awareness about environmental protection, low-carbon tourism (LCT) is viewed as a new form of sustainable development that can provide greater economic, social and environmental benefits. Evaluating tourism attractions is of great significance for operators of tourism attractions to improve the service quality. Meanwhile, tourists can select the most appropriate LCT scenic spots among the alternatives. To address this decision-making problem, this study develops an integrated multi-criteria group decision-making method within the context of multi-granular linguistic distribution assessments. First, the best-worst method is employed to identify the weights of the criteria. Second, an extended relative entropy-based method that combines a proximity entropy weight and a similarity entropy weight is developed to assign weights to decision-makers in terms of each criterion. Third, an improved multi-granular linguistic distribution ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) is proposed to prioritize LCT attractions. Finally, an illustrative example of LCT attraction evaluation followed by comparative and sensitivity analyses is presented to verify the applicability and effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is an effective customer-oriented technique for enhancing healthcare service quality. Implementing QFD, patient requirements (PRs) should be firstly identified and prioritized to satisfy patients' expectation. PRs expressed by human language should be interpreted and converted into technical requirements (TRs) under uncertain situations. The prioritization of TRs should consider patients' bounded rationality ensuring the quality enhancement fit their psychological behaviors. This paper proposes a cloud-support QFD model to improve above phases. First, a best-worst method based PRs prioritizing phase is built to generate reliable and consistent weights of PRs. Then, a translating phase is constructed based on linguistic distribution assessments and asymmetric normal clouds to interpret patients' voice and model the correlations. The phase of prioritizing TRs is proposed based on TODIM to better reflect patients' psychological behaviors. Finally, a case study, sensitive and comparative analyses are provided to show the reliability of the proposed model. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM), which involves dozens to hundreds of decision-makers (DMs), is attracting extensive attention and has become an interesting and hot topic in recent years. Because of various backgrounds and expression habits, DMs tend to elicit preferences with different preference representation structures. Moreover, due to various attitudes and interests, some DMs may adopt noncooperative behaviors to further benefit themselves in LSGDM. To cope with these issues, this study develops an adaptive consensus framework to support heterogeneous LSGDM. A cosine similarity based optimization model is constructed and its analytical solution is derived to directly obtain the collective priority vector of the group using heterogeneous preferences. An extended k-means algorithm is then used to classify DMs. Subsequently, a two-stage uninorm-based behavior management method is developed to generate personalized weight feedback to each DM and subcluster according to their cooperative and noncooperative degrees in the consensus-reaching process. Finally, an illustrative example followed by simulation and comparative analyses is provided to reveal the advantages and features of the proposed approach. [ABSTRACT FROM AUTHOR]

The consensus‐reaching process (CRP) to achieve higher unanimity and ensure common agreement before deriving a final decision has become an important procedure in group decision‐making problems. The demand for high‐quality decision results has motivated the development of large‐scale group decision‐making (LGDM). In such cases, the issue of minority opinion has gained awareness due to the related effects on enhancing consensus and decision quality. A minority opinion cannot exert an effect unless the majority attach importance to whether that opinion is supported or not. To reflect the effect of minority opinions on consensus, this paper establishes a LGDM framework with an objective and interactive‐information‐based feedback mechanism for the CRP. Given the natural forms of human expression, multi‐granular linguistic information and a 2‐tuple linguistic model are used. First, initial weights are objectively assigned to decision‐makers (DMs) to weaken the impact of the majority. Subsequently, a non‐support degree function is newly defined to reflect the extent to which other DMs dissent from a minority opinion. More importantly, feedback rules are constructed to make corresponding adjustments to the powers of discourse among all DMs in the attempt to reach consensus. Finally, the proposed three‐phase LGDM framework is applied to new product development (NPD), and simulation experiments are conducted based on two algorithms to verify the framework's applicability and feasibility. [ABSTRACT FROM AUTHOR]

This study aims to develop an integrated approach for solving probabilistic linguistic multi-criteria decision-making (MCDM) problems. This study first reveals the limitations in the existing methods for probabilistic linguistic term sets (PLTSs). Subsequently, an improved aggregation method and a novel ranking method are developed for addressing PLTSs. The proposed aggregation method is based on the evidence reasoning algorithm and the proposed ranking method that integrates with a three-fold ranking method is based on optimism, neutralism and pessimism decision-making processes. Thus, the proposed approach can straightforwardly and robustly deal with probabilistic linguistic MCDM problems considering decision-makers' psychological preferences. Moreover, to flexibly obtain criteria weights, several models are constructed to adapt to different decision-making situations, in which criteria weight information is incompletely, inconsistently or completely unknown. Finally, a case study on selecting the best investment objective(s) among the member counties of "One Belt, One road" is conducted to validate the feasibility and effectiveness of the proposed approach, followed by a comparative analysis between the existing methods and the proposed approach. [ABSTRACT FROM AUTHOR]

Uncertain linguistic Z-numbers (ULZNs), which inherit the prominent characteristics of linguistic term sets and Z-numbers, can flexibly describe qualitative information as well as its reliability. To cautiously solve a qualitative multi-criteria decision-making (MCDM) problem with larger number of criteria than alternatives, this paper develops an ULZN-based QUALIFLEX (QUALItative FLEXible multiple criteria method) by considering the decision-maker (DM)'s psychological behavior character. First, the likelihood and diversity degree of ULZNs are determined and a comparison method is proposed. Second, a decision model combining the QUALIFLEX and prospect theory is developed to address MCDM problems with ULZNs, considering the incomplete compensation of criteria. An extended maximizing deviation method is developed to objectively obtain the weights of the criteria. Subsequently, an illustrative example concerning risk evaluation of high-tech project investment with larger number of criteria than alternatives is provided to demonstrate the application of the proposed approach. Finally, sensitivity analysis and comparative analyses are conducted to validate the proposed approach. The result shows that the proposed approach can effectively address MCDM problems with ULZNs, considering the DM's psychological behavior. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets are powerful techniques for modeling vagueness in practice. The aim of this article is to investigate an effective means to aggregate uncertain information and then employ it into settling multiple criteria decision making (MCDM) problems within the Pythagorean fuzzy circumstances. To capture the nature of the reality, some special cases should be comprehensively considered. First, though correlation commonly exist among criteria, a deep insight should also be provided into some realistic situations, in which not all the criteria are interrelated to others. Besides, it is more reasonable to take the importance of the input arguments into consideration. Effected by aforementioned point, this article explores a Pythagorean fuzzy partitioned normalized weighted Bonferroni mean (PFPNWBM) operator with the combination of partitioned Bonferroni mean (BM) and normalized weighted BM operators considering Shapley fuzzy measure. Subsequently, in the context of partially known weight information, models are established to identify the optimal Shapley fuzzy measure. Moreover, integrated the PFPNWBM operator with the optimal Shapley fuzzy measure identification model, a Pythagorean fuzzy MCDM approach is designed. Finally, an illustrative example and detailed analyses are performed to demonstrate its feasibility and reliability. [ABSTRACT FROM AUTHOR]

Abstract This study focuses on consensus-reaching in social network group decision-making. Using Archimedean t- norms and t- conorms as the basis, this study introduces a family of interval-valued dual trust propagation (IVDTP) operators. A perfect knowledge degree-induced ordered weighted averaging (PKD-IOWA) aggregation operator is also put forward for obtaining the overall trust and distrust values. The proposed PKD-IOWA aggregation operator makes full use of the knowledge in each trust path, which can effectively overcome the limitations of existing trust aggregation operators. The weights of the experts can then be derived by combining the IVDTP operators and the PKD-IOWA aggregation operator. Three levels of consensus indices are defined to identify the incompatible experts in a group. A two-fold feedback mechanism comprising a judgment feedback mechanism (JFM) and a weight feedback mechanism (WFM), is designed to reach consensus. In each round of this interaction model, the most incompatible expert is advised to modify his/her judgments on the basis of the JFM. If the expert rejects the suggestion, then his/her weight will be reduced according to the WFM, which can act as an incentive or penalty to force him/her to accept the judgment feedback suggestion. Two illustrative examples are used to validate the feasibility and effectiveness of the proposed approach, followed by comparative analysis and discussion. [ABSTRACT FROM AUTHOR]

Abstract The sustainability challenge is increasingly driving the adoption of supercritical water gasification (SCWG) technology to ensure the elimination and recovery of pollution produced by sewage sludge treatment (SST). Risk evaluation by failure mode and effects analysis (FMEA) plays a crucial role in guaranteeing the reliability and safety of SCWG systems. However, some limitations in existing FMEA methods need to be ameliorated. Multiple risk factors are involved in prioritizing risk levels for failure modes in SCWG systems, it is essential a multiple criteria decision making (MCDM) process, in which overall assessments of failure modes should be provided according to their performances from several points during a system operation period. Due to differences in knowledge backgrounds and experiences, FMEA team members prefer to utilize multi-granular linguistic term sets to express their assessments of system risk. A hybrid risk evaluation model by FMEA is exploited with multi-granular linguistic distribution assessments to suit practical case. Best-worst and maximizing derivation methods are adopted to determine subjective and objective combined weights for distinguishing the importance of risk factors. Complex proportional assessment method is used to prioritize failure modes for explicitly and effectively reflecting the risk level of each failure mode. The proposed model is applied in a practical case of an SCWG system used in SST. Results derived from comparative and sensitivity analyses fully demonstrate the reliability and validity of the model. [ABSTRACT FROM AUTHOR]

Faced with risks aroused by the ever-increasing expansion of industrialization and urbanization, water security sustainability takes more and more effect on balancing regional sustainability and development, especially for industrial regions. Introducing sustainability into the indicator framework of water security is an indispensable issue should be addressed. The indicators of water security sustainability in practice are measured by observation data obtained from a monitoring period. These data are commonly expressed in various forms, such as ratio and qualitative linguistic, which makes them hard to be quantified and evaluated. As multiply influential criteria are involved the water security sustainability evaluation process, it can be treated as a multi-criteria decision making problem. An appropriate evaluation framework with scientific and strategic multi-criteria decision making techniques is supposed to be constructed. This paper incorporates the dimensions related to sustainability into the indicator framework of water security sustainability. Another contribution of this paper is that it establishes an observation data conversion standard and adopts linguistic distribution assessments as information representation. Linguistic distribution assessments can capture the natural characteristics of the observation data of water security sustainability indicators. More importantly, a multistage decision support framework integrated with multi-criteria decision making techniques, such as best-worst method (BWM), decision making trial and evaluation laboratory (DEMATEL) and technique for order performance by similarity to ideal solution (TOPSIS), is constructed for water security sustainability evaluation. Then, deep analyzes about the evaluation results and influential factors are provided, thereby certifying the reliability of evaluation results and facilitating the enhancement of water security sustainability. [ABSTRACT FROM AUTHOR]

Abstract: The main purpose of this paper is to provide a multicriteria decision‐making (MCDM) approach that applies the gray linguistic Bonferroni mean (BM) operator to address the situations where the criterion values take the form of gray linguistic numbers (GLNs) and the criterion weights are known. First, the related operations and comparison method for GLNs are provided. Subsequently, a BM operator and weighted BM operator of GLNs are developed. Then, based on the gray linguistic weighted BM operator, an MCDM approach is proposed. Finally, an illustrative example is given and a comparison analysis is conducted between the proposed approach and other existing methods to demonstrate the effectiveness and feasibility of the developed approach. [ABSTRACT FROM AUTHOR]

Abstract: In selecting logistics service providers, the evaluation criteria can be easily prioritized and possibly interrelated with each other, and the assessment of alternatives under qualitative criteria is usually accomplished by more than one decision maker. A novel multicriteria decision‐making approach with multihesitant fuzzy linguistic term elements (MHFLTEs) based on the Heronian mean (HM) and prioritized average operators can effectively deal with the problems inherent in such a scenario. Multihesitant fuzzy linguistic term sets (MHFLTSs) were proposed on the basis of multihesitant fuzzy sets (MHFSs) and hesitant fuzzy linguistic sets (HFLSs), where each MHFLTE can contain nonconsecutive and repeated linguistic terms. Using MHFLTEs, one decision maker can provide one or several consecutive linguistic terms in evaluating an alternative under one specific criterion, different decision makers’ evaluation values can be collected, and the frequency of a linguistic term in the evaluation information can accord with reality. This paper revises the basic operations and comparison method for MHFLTEs on the basis of the originals and defines some multihesitant fuzzy linguistic HM operators for MHFLTEs to deal with problems in which weight information cannot be accurately established for criteria, but their priorities can be provided in groups or without groups. Finally, the validity and effectiveness of the proposed approach are demonstrated through an illustration of a logistics outsourcing problem and a comparison analysis. [ABSTRACT FROM AUTHOR]

A simplified neutrosophic uncertain linguistic set that integrates quantitative and qualitative evaluation can serve as an extension of both an uncertain linguistic variable and a simplified neutrosophic set. It can describe the real preferences of decision-makers and reflect their uncertainty, incompleteness and inconsistency. This paper focuses on multi-criteria decision-making (MCDM) problems in which the criteria occupy different priority levels and the criteria values take the form of simplified neutrosophic uncertain linguistic elements. Having reviewed the relevant literatures, this paper develops some generalized simplified neutrosophic uncertain linguistic prioritized weighted aggregation operators and applies them to solve MCDM problems. Finally, an illustrative example is given, and two cases of comparison analysis are conducted with other representative methods to demonstrate the effectiveness and feasibility of the developed approach. [ABSTRACT FROM AUTHOR]

The smart bike-sharing program (BSP), which is a novel bike-sharing mode of traditional BSP, has become popular in many cities in China. However, most BSP studies focus only on the socio-economic characteristics of the users, bike-sharing patterns, and impacts that influence bike-sharing demand. This study increases the knowledge on BSPs by presenting a hybrid fuzzy multi-criteria decision-making approach that is based on multi-phase quality function deployment. This approach covers the performance evaluation of smart BSPs allowing for customer voices under uncertain conditions. This method combines the fuzzy best-worst method (BWM), fuzzy maximizing deviation method (MDM), and fuzzy multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA). It uses fuzzy BWM to determine priorities of customer requirements and company strategies and to obtain subjective importance coefficients of criteria. Fuzzy MDM is used to derive objective importance coefficients of criteria and fuzzy MULTIMOORA is used to rank alternatives in terms of each criterion. An empirical case in Changsha, which is one of the pilot areas for comprehensively supporting the reform for an environment-friendly and resource-conserving society ("two-oriented society") in China, is provided to illustrate the application of the proposed framework. Results can help enterprises and organizations improve their performance and provide high-quality products and services for customers. [ABSTRACT FROM AUTHOR]

Market segment evaluation and selection (MSE/MSS) become one of the most essential elements in marketing practices of enterprises, since they assist enterprises in developing homogeneous market segments. In this paper, a hybrid single-valued neutrosophic multi-criteria group decision-making (MCGDM) approach with quality function deployment (QFD) is used to support theMSE/MSSprocess. Firstly, single-valued neutrosophic numbers are used to represent the decisionmakers' qualitative assessments. Secondly, an extended QFD is used to translate the customer requirements (CRs) into relevant design requirements (DRs), and a single-valued neutrosophic DEMATEL (DEcision-MAking Trial and Evaluation Laboratory) is used to determine the importance weights of CRs and DRs. Thirdly, a single-valued neutrosophic TODIM (An acronym in Portuguese of interactive and multi-criteria decision-making) is employed to rank all the alternatives. Subsequently, a case study of MSE/MSS of a smart bike-sharing company is presented. Finally, some valuable managerial suggestions are provided through the results of sensitivity analysis and comparative analysis. [ABSTRACT FROM AUTHOR]

Multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) is a useful method to apply in multi-criteria decision-making due to the flexibility and robustness it introduces into the decision process. This paper defines several simplified neutrosophic linguistic distance measures and employs a distance-based method to determine criterion weights. Then, an improved MULTIMOORA approach is presented by integrating the simplified neutrosophic linguistic normalized weighted Bonferroni mean and simplified neutrosophic linguistic normalized geometric weighted Bonferroni mean operators as well as a simplified neutrosophic linguistic distance measure. This approach ranks alternatives according to three ordering methods, and then, uses dominance theory to combine the three rankings into a single ranking. Finally, this paper presents a practical case example and conducts a comparative analysis between the proposed approach and existing methods in order to verify the feasibility and effectiveness of the developed methodology. [ABSTRACT FROM AUTHOR]

In this paper, two optimisation models are established to determine the criterion weights in multi-criteria decision-making situations where knowledge regarding the weight information is incomplete and the criterion values are interval neutrosophic numbers. The proposed approach combines interval neutrosophic sets and TOPSIS, and the closeness coefficients are expressed as interval numbers. Furthermore, the relative likelihood-based comparison relations are constructed to determine the ranking of alternatives. A fuzzy cross-entropy approach is proposed to calculate the discrimination measure between alternatives and the absolute ideal solutions, after a transformation operator has been developed to convert interval neutrosophic numbers into simplified neutrosophic numbers. Finally, an illustrative example is provided, and a comparative analysis is conducted between the approach developed in this paper and other existing methods, to verify the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Qualification for the importance degree of service designs (SDs) is an essential stage in promoting accommodation performances to satisfy customers and gain market shares. Although various methods have been applied to derive the priorities of SDs, they do not effectively deal with the vagueness of information and the heterogeneity of decision makers (DMs). This study presents an improved quality function deployment (QFD) for prioritizing SDs. Multigranular unbalanced linguistic term sets are used to capture evaluators' ratings to cope with vague information. The multigranular linguistic information is unified by using a novel uniform method. This kind of information then is represented as linguistic distribution assessments using the concept of random preferences based on possibility interpretations of weight information. A consensus‐based mathematical programming model is established to determine the weights of DMs. Moreover, a combined structure that combines maximizing deviation and best worst methods is used to derive basic weights of SDs. Finally, an illustrative example of island accommodation management in Weizhou Island is provided to demonstrate the applicability and advantages of the proposed QFD framework. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop an integrated approach for solving multicriteria group decision‐making problems with multigranular unbalanced hesitant fuzzy linguistic term sets (HFLTSs). Firstly, a signed distance‐based transformation function is proposed to unify multigranular unbalanced hesitant fuzzy linguistic (HFL) assessments. Secondly, a mathematical programming model based on the maximum consensus is constructed to allocate decision‐makers (DMs)' weights objectively. Thirdly, a new signed distance‐based preference score function is defined to aggregate HFL assessments and determine the weak ranking of alternatives, and a novel preference, indifference, and incomparability test framework is constructed to identify the subtle relations among alternatives. On these bases, a signed distance‐based ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) method, in which knowledge regarding criterion values and weights are expressed as multigranular unbalanced HFLTSs, is developed to obtain the ranking of alternatives. Finally, an illustrative example, followed by sensitivity and comparative analyses, is presented to verify the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]