论文标题
基于时间序列特征的多准则决策分析的研究和TOSSIS方法提案，以进行紧张方法
A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach
论文作者
论文摘要
已经开发了许多多个标准决策分析（MCDA）方法，以根据几个决策标准对替代方案进行排名。通常，MCDA方法处理决策时的标准价值，而无需考虑其演变。但是，考虑标准的时间序列以来，可能是重要的，因为提供了决策的基本信息（例如，标准的提高）。为了解决这个问题，我们提出了一种新方法，以根据标准时间序列特征（趋势，差异等）对替代方案进行排名。在这种新颖的方法中，数据以三个维度结构，需要更复杂的数据结构，作为\ textIt {张量}，而不是MCDA中使用的经典矩阵表示。因此，我们为Topsis方法提出了一个扩展，以处理张量而不是矩阵。计算结果表明，通过考虑有意义的决策信息，可以从新角度对替代方案进行排名。
A number of Multiple Criteria Decision Analysis (MCDA) methods have been developed to rank alternatives based on several decision criteria. Usually, MCDA methods deal with the criteria value at the time the decision is made without considering their evolution over time. However, it may be relevant to consider the criteria' time series since providing essential information for decision-making (e.g., an improvement of the criteria). To deal with this issue, we propose a new approach to rank the alternatives based on the criteria time-series features (tendency, variance, etc.). In this novel approach, the data is structured in three dimensions, which require a more complex data structure, as the \textit{tensors}, instead of the classical matrix representation used in MCDA. Consequently, we propose an extension for the TOPSIS method to handle a tensor rather than a matrix. Computational results reveal that it is possible to rank the alternatives from a new perspective by considering meaningful decision-making information.
