Dealing with inconsistent judgments in multiple criteria sorting models.

Sorting models consist in assigning alternatives evaluated on several criteria to ordered categories. To implement such models it is necessary to set the values of the preference parameters used in the model. Rather than fixing the values of these parameters directly, a usual approach is to infer these values from assignment examples provided by the decision maker (DM), i.e., alternatives for which (s)he specifies a required category. However, assignment examples provided by DMs can be inconsistent, i.e., may not match the sorting model. In such situations, it is necessary to support the DMs in the resolution of this inconsistency. In this paper, we extend algorithms from Mousseau et al.(2003) that calculate different ways to remove assignment examples so that the information can be represented in the sorting model. The extension concerns the possibility to relax (rather than to delete) assignment examples. These algorithms incorporate information about the confidence attached to each assignment example, hence providing inconsistency resolutions that the DMs are most likely to accept.Multicriteria decision aiding; Inconsistency analysis; Sorting problem;

Co-constructive development of a green chemistry-based model for the assessment of nanoparticles synthesis

Nanomaterials (materials at the nanoscale, 10−9m) are extensively used in several industry sectors due to the improved properties they empower commercial products with. There is a pressing need to produce these materials more sustainably. This paper proposes a Multiple Criteria Decision Aiding (MCDA) approach to assess the implementation of green chemistry principles as applied to the protocols for nanoparticles synthesis. In the presence of multiple green and environmentally oriented criteria, decision aiding is performed with a synergy of ordinal regression methods; preference information in the form of desired assignment for a subset of reference protocols is accepted. The classification models, indirectly derived from such information, are composed of an additive value function and a vector of thresholds separating the pre-defined and ordered classes. The method delivers a single representative model that is used to assess the relative importance of the criteria, identify the possible gains with improvement of the protocol’s evaluations and classify the non-reference protocols. Such precise recommendation is validated against the outcomes of robustness analysis exploiting the sets of all classification models compatible with all maximal subsets of consistent assignment examples. The introduced approach is used with real-world data concerning silver nanoparticles. It is proven to effectively resolve inconsistency in the assignment examples, tolerate ordinal and cardinal measurement scales, differentiate between inter- and intra-criteria attractiveness and deliver easily interpretable scores and class assignments. This work thoroughly discusses the learning insights that MCDA provided during the co-constructive development of the classification model, distinguishing between problem structuring, preference elicitation, learning, modeling and problem-solving stages

A new parsimonious AHP methodology: assigning priorities to many objects by comparing pairwise few reference objects

We propose a development of the Analytic Hierarchy Process (AHP) permitting to use the methodology also for decision problems with a very large number of alternatives and several criteria. While the ap- plication of the original AHP method involves many pairwise comparisons between considered objects, that can be alternatives with respect to considered criteria or criteria between them, our parsimonious proposal is composed of five steps: (i) direct evaluation of the objects at hand; (ii) selection of some reference objects; (iii) application of the original AHP method to the reference objects; (iv) check of the consistency of the pairwise comparisons of AHP and the compatibility between the rating and the prior- itization with a subsequent discussion with the decision maker who can modify the rating or pairwise comparisons of reference objects; (v) revision of the direct evaluation on the basis of the prioritization supplied by AHP on reference objects. Our approach permits to avoid the distortion of comparing more relevant objects (reference points) with less relevant objects. Moreover, our AHP approach avoids rank reversal problems, that is, changes of the order in the prioritizations due to adding or removing one or more objects from the set of considered objects. The new proposal has been tested and experimentally validated