Zhang-peng Tian

Multi-criteria decision-making method based on a cross-entropy with interval neutrosophic sets

ABSTRACT 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.

A Likelihood-Based Qualitative Flexible Approach with Hesitant Fuzzy Linguistic Information

The qualitative flexible multiple criteria method (QUALIFLEX) is a useful outranking method for multi-criteria decision analysis due to its flexibility in regard to cardinal and ordinal information. This paper puts forward an extended QUALIFLEX approach with a new likelihood-based comparison method to address multi-criteria decision-making problems in a hesitant fuzzy linguistic environment. The rankings produced by our new comparison method are more convincing than those obtained by existing methods, such as likelihood, distance measures, and the score function of hesitant fuzzy linguistic term sets or hesitant fuzzy linguistic elements. The proposed QUALIFLEX model, which is based on the likelihood-based comparison method, can measure the level of concordance or discordance of the complete preference order for tackling multi-criteria decision-making problems. Finally, two cases are presented as a comparative analysis between the proposed approach and other related methods. This example demonstrates the effectiveness and flexibility of the proposed methodology in the context of hesitant fuzzy linguistic information.

A multihesitant fuzzy linguistic multicriteria decision‐making approach for logistics outsourcing with incomplete weight information

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.

Multicriteria decision-making approach based on gray linguistic weighted Bonferroni mean operator

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.

Multi-criteria decision-making based on generalized prioritized aggregation operators under simplified neutrosophic uncertain linguistic environment

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.

An improved MULTIMOORA approach for multi-criteria decision-making based on interdependent inputs of simplified neutrosophic linguistic information

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.

Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment

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.

An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods

Abstract In general, analysis of failure modes and their effects requires a group of experts to tackle substantial uncertainties associated with the risk evaluation process. To date, to overcome one or more of the uncertainty-related issues, an increasing number of failure mode and effects analysis (FMEA) models based on multi-criteria decision-making (MCDM) methods have been developed. However, most of the improvements have not cautiously considered the process of assigning importance weights to risk factors and FMEA team members during FMEA. This study aims to enhance the performance of the classic FMEA and to propose an integrated fuzzy MCDM approach for FMEA. First, a fuzzy best-worst method is used to obtain the weights of risk factors. Second, an integrated structure based on fuzzy proximity and fuzzy similarity entropy weights is developed to obtain the weights of FMEA team members with respect to different risk factors. Finally, a fuzzy VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje) approach is employed to obtain the risk priorities of failure modes. The applicability and effectiveness of the proposed approach is validated through an illustrative example concerning risk analysis of a grinding wheel system. The results of sensitivity and comparative analyses show that the proposed approach is valid and can provide valuable and effective information in assisting risk management decision-making.

Signed distance-based consensus in multi-criteria group decision-making with multi-granular hesitant unbalanced linguistic information

Abstract This study aims to deal with hesitant fuzzy linguistic multi-criteria group decision-making (MCGDM) problems with multi-granular unbalanced linguistic term sets. Firstly, a signed distance measure is developed as the support tool for hesitant fuzzy linguistic term sets. This measure is based on the ordinal semantics of linguistic terms and the possibility distribution method. In this manner, the signed distance measure can release the strict constraint of symmetric and uniform linguistic term sets in qualitative decision-making. Secondly, a signed distance-based transformation function is proposed to unify the multi-granular hesitant unbalanced linguistic assessments. Moreover, comprehensive consensus measures on three levels are proposed to measure the consensus degree. These measures simultaneously consider the consensus degree between individual and collective preference and between experts. Subsequently, a consensus reaching algorithm is designed and incorporated into the proposed MCGDM approach. Lastly, an illustrative example, followed by an in-depth comparative analysis and discussion is presented to demonstrate the proposed approach. Results show that the proposed approach can flexibly and effectively tackle MCGDM problems with multi-granular hesitant unbalanced linguistic information.

A two-fold feedback mechanism to support consensus-reaching in social network group decision-making

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.