Using a segmenting description approach in multiple criteria decision aiding Abstract   We propose a new method for analyzing a set of parameters in a multiple criteria ranking method. Unlike the existing techniques, we do not use any optimization method, instead incorporating and extending a Segmenting Description (SD) approach. The algorithm rewrites a set of initial inequalities into an equivalent SD system by means of three fundamental operations in a way that makes it possible to either easily analyze all feasible solutions or identify all sources of inconsistency. While considering a valuebased preference disaggregation method, we demonstrate the usefulness of the introduced method in a multipurpose decision analysis exploiting a system of inequalities that models the Decision Makerxe2x80x99s (DMxe2x80x99s) preferences. We focus on the contexts where informative results can be obtained through the analysis of (in)feasibility of such systems. Specifically, we discuss how SD can be applied for verifying the consistency between the revealed and estimated preferences as well as for identifying the reasons of potential incoherence. Moreover, we employ the method for conducting robustness analysis, i.e., discovering a set of all compatible parameter values and verifying the stability of suggested recommendation in view of multiplicity of feasible solutions. In addition, we make clear its suitability for generating arguments about the validity of outcomes and the role of particular criteria. Overall, the SD approach confirms its usefulness in analyzing the relationships between the DMxe2x80x99s preference information, the compatible parameters of an assumed model, and the valuedriven ranking procedure. We also discuss the favourable characteristics of the proposed method enhancing its suitability for use in Multiple Criteria Decision Aiding. These include possibility of studying any system of inequalities without an objective function, keeping in memory an entire process of transforming a system of inequalities that makes identification of potential incoherence straightforward, and avoiding the need for processing the inequalities contained in the basic system which is subsequently enriched with some hypothesis to be verified. The latter is substantial for saving time when a multitude of hypotheses need to be checked in the same conditions. The applicability and the advantages of using the proposed method in the decision aiding context are clearly exemplified on a numerical study.

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.