ISOCOV: a new MCDM method to handle value constraints in Web service selection The aim of the current research is to define a new MCDM method that has the ability to rank Web services when value constraints are expressed on QoS criteria. This method is called, ISOCOV (Ideal SOlution with COnstraint on Values). In presence of values constraints, the majority of existing MCDM methods proceeds either by discarding from the research space all the non satisfactory Web services, or by providing a partial integration of the requirements in the decision making process. On the contrary, ISOCOV takes into account all the aspects thus providing to the user a more accurate solution while dealing with the given constraints. The purpose is to be able to promote services that do not meet the constraints, when the cleaned research space stands empty or not representative enough. A case study using a real dataset is considered to highlight the functioning and the accuracy of our method comparatively to other similar MCDM approaches.

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.