Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes, and services. However, the conventional FMEA method has been criticized for inherent drawbacks that limit effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members’ weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison to existing methods.

Improving Risk Evaluation in FMEA With Cloud Model and Hierarchical TOPSIS Method

A consensus model for hesitant fuzzy preference relations and its application in water allocation management

Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method

Hesitant fuzzy linguistic preference relation (HFLPR) is a new preference structure that the decision makers (DMs) are hesitant about several possible linguistic terms of preference information for pairwise comparison between alternatives. This paper examines the additive consistency of HFLPR with a new expansion principle for hesitant fuzzy linguistic term sets (HFLTSs). In order to normalize HFLTSs with different numbers of linguistic terms, a least common multiple expansion (LCME) principle is proposed. According to the LCME principle, the additive consistent index of an HFLPR is defined to measure the consistency level of the HFLPR. For improving the unacceptable additive consistency of an HFLPR, a pure integer programming model is constructed to derive an acceptable additive consistent HFLPR. In group decision making (GDM) with HFLPRs, the similarities between DMs are calculated based on their individual HFLPR with acceptable additive consistency. Subsequently, the confidence degrees of DMs are defined to derive DMs’ weights, and some examples including an investment project management problem are analyzed to verify the effectiveness of the proposed method.

Additive Consistency of Hesitant Fuzzy Linguistic Preference Relation With a New Expansion Principle for Hesitant Fuzzy Linguistic Term Sets

EDAS (Evaluation Based on Distance from Average Solution) is based on the distances of each alternative from the average solution. It is similar to other distance based multi-attribute decision-making methods such as TOPSIS and VIKOR. Hesitant fuzzy sets are an extension of ordinary fuzzy sets where the hesitation arises in the assignment of membership degrees of the elements to a fuzzy set. In this paper, we extend classical EDAS method to its hesitant fuzzy version in order to capture decision makers’ hesitancies. The proposed Hesitant Fuzzy Evaluation Based on Distance from Average Solution (HF-EDAS) is based on different aggregation operators with defuzzification and without-defuzzification processes, which is presented by four HF-EDAS versions. The proposed method has been applied to a multi-criteria and multi-expert hospital selection problem for organ transplantation. Additionally, we present a comparative analysis with hesitant fuzzy TOPSIS (HF-TOPSIS). The results show that HF-EDAS selects the same best alternative as HF-TOPSIS. However, the proposed versions of HF-EDAS indicated some slight changes in the ranking of alternatives.

A Novel Hesitant Fuzzy EDAS Method and Its Application to Hospital Selection

Considering that there may exist some interactions between membership function and non-membership function of different dual hesitant fuzzy sets (DHFSs), in this paper we present some new operational laws from the probability point of view and give a geometric interpretation of the new operations. Based on which, a new class of dual hesitant fuzzy aggregation operators are developed, including dual hesitant fuzzy weighted interaction averaging operator, dual hesitant fuzzy weighted interaction geometric operator, dual hesitant fuzzy ordered weighted interaction averaging operator, dual hesitant fuzzy ordered weighted interaction geometric operator, dual hesitant fuzzy hybrid interaction averaging operator, and dual hesitant fuzzy hybrid interaction geometric operator. The properties of these aggregation operators are investigated. The key advantage of these operators is that the interactions between non-membership function and membership function of different DHFSs are considered, and much more informatio...

Dual hesitant fuzzy interaction operators and their application to group decision making

The consensus reaching process is a dynamic and iterative process for improving group's consensus level before making a final decision in group decision-making GDM. As the experts will express their opinions under their own intellectual level from different aspects, it is natural that the experts’ weights should reflect their judgment information. This paper proposes a dynamic way to adjust weights of decision-makers DMs automatically when they are asked to give original judgment information for GDM problems, in which the DMs express their judgment information by hesitant fuzzy preference relations HFPRs. Two indices, an individual consensus index of hesitant fuzzy preference relation ICIHFPR and a group consensus index of hesitant fuzzy preference relation GCIHFPR, are introduced. Normalisation of HFPRs with different numbers of possible values is taken into consideration for better computation and comparison. An iterative consensus reaching algorithm is presented with DMs’ weighting vector changing in each consensus reaching process and the process terminates until both the ICIHFPR and GCIHFPR are controlled within predefined thresholds. Finally, an example is illustrated and comparative analyses demonstrate the effectiveness of the proposed methods.

A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations

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Dual hesitant fuzzy interaction operators and their application to group decision making

A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations