Mission Reliability Allocation Based on IntervalHesitant Fuzzy Linguistic Term Sets Reliability allocation, especially mission reliability allocation, is very important to system reliability design. Equal allocation method and scoring allocation method are the most commonly used methods in practical engineering. The former can only used for series system and simple system. The latter is more useful. However, it is difficult to give the accurate scores for experts because of the incompleteness and uncertainty of information, which has a great effect on the result. Therefore, a new system mission reliability allocation method based on the interval hesitant fuzzy linguistic term set was proposed. First, the interval hesitation fuzzy scoring data of expert was processed. Considering the consistency weights and empirical weights of expert opinions, respectively, the comprehensive expert weight was obtained. Then, mission reliability allocation methods based on the expert score above for different reliability logic relations including series system, parallel system, kn system, and standby system, were given respectively. Finally, comparing the calculation results of the proposed method with the traditional method, an example was analyzed to verify the feasibility and effectiveness of the method.

Multicriteria outranking method based on probability distribution with probabilistic linguistic information Abstract   The probabilistic linguistic term set (PLTS) is a powerful tool for describing qualitative evaluations derived from teams of experts, and it has adequate description capability in identifying preferences among different evaluations. The structure of PLTSs is complex, however, and many existing studies do not deal with probabilistic linguistic information appropriately. Hence, this study explores the simple and effective processing of PLTSs and develops an applicable multicriteria decisionmaking (MCDM) method to address realworld problems. First, PLTSs are characterised as probability distributions, and the corresponding cumulative distribution functions are presented. In this manner, the concordance and discordance indices of PLTSs are defined by the systematic comparison between different cumulative distribution functions. Subsequently, four kinds of novel binary relations for PLTSs are proposed. Then, an innovative multicriteria outranking method is developed by modelling pseudocriteria and implementing outranking aggregation and exploitation. Finally, an illustrative example concerning new energy selection is provided to elucidate the application of the developed method. The strengths of this method are verified further by some analyses and discussions.