Comparison of tools for the sustainability assessment of nanomaterials

Nanomaterials are becoming widely used in areas such as biomedical applications, food, environmental protection, energy production, information technology and agriculture. As such, more research has been conducted on their synthesis and manufacturing from a variety of feedstocks. However, concerns regarding their impact on human health and the environment leads researchers to conduct a variety of ‘sustainability’ assessments. The purpose of this paper was to review the current opinion of sustainability assessments concerning nanomaterials. Major assessment tools were reviewed including life cycle assessment, risk assessment and multi-criteria decision analysis, along with subcategories. The review found that each assessment tool did positively contribute to sustainability assessments, but each also had drawbacks of varying degrees. In particular, multi-criteria decision analysis provides the most relevant tool for conducting a sustainability assessment as it can handle criteria of any typology and provide multiple types of decision recommendations, including rankings, scores and classifications

Robust ordinal regression for value functions handling interacting criteria

International audienceWe present a new method called UTAGMS–INT for ranking a finite set of alternatives evaluated on multiple criteria. It belongs to the family of Robust Ordinal Regression (ROR) methods which build a set of preference models compatible with preference information elicited by the Decision Maker (DM). The preference model used by UTAGMS–INT is a general additive value function augmented by two types of components corresponding to ‘‘bonus’’ or ‘‘penalty’’ values for positively or negatively interacting pairs of criteria, respectively. When calculating value of a particular alternative, a bonus is added to the additive component of the value function if a given pair of criteria is in a positive synergy for performances of this alternative on the two criteria. Similarly, a penalty is subtracted from the additive component of the value function if a given pair of criteria is in a negative synergy for performances of the considered alternative on the two criteria. The preference information elicited by the DM is composed of pairwise comparisons of some reference alternatives, as well as of comparisons of some pairs of reference alternatives with respect to intensity of preference, either comprehensively or on a particular criterion. In UTAGMS–INT, ROR starts with identification of pairs of interacting criteria for given preference information by solving a mixed-integer linear program. Once the interacting pairs are validated by the DM, ROR continues calculations with the whole set of compatible value functions handling the interacting criteria, to get necessary and possible preference relations in the considered set of alternatives. A single representative value function can be calculated to attribute specific scores to alternatives. It also gives values to bonuses and penalties. UTAGMS–INT handles quite general interactions among criteria and provides an interesting alternative to the Choquet integral

Apprentissage d’intégrales de Sugeno à partir de données inconsistantes

National audienceThe basic setting of this article is multicriteria decision making and preference aggregation. The problem treated is that of learning a Sugeno integral from inconsistent data, where values are elements of a totally ordered set. This is a difficult optimization problem : indeed, a Sugeno integral is determined by 2^n values, with n being the number pf parameters. In this article we propose two learning methods : the first one is an application of simulated annealing, and the second is a new algorithm which relies on the selection of a consistant subset of data and for which the value of n doesn't affect the running time significantly.En prenant pour cadre de référence l'aidè a la décision multi-critères et l'agrégation de préférences, cet article traite de l'apprentissage de l'intégrale de Sugenò a partir de données inconsistantes, et dont les valeurs appartiennent à un ensemble totalement ordonné. Il s'agit d'un problème d'optimisation difficile, puisqu'une intégrale de Sugeno est définie d'après 2^n valeurs, où n est le nombre de paramètres. Dans cet article nous considérons deux méthodes : la premìère est une application du recuit simulé, et la seconde est un nouvel algorithme reposant sur la séléction préalable d'un sous-ensemble de données consistantes, dont le temps d'exécution est peu sensible à la valeur de n

A systematic review on MIVES: a sustainability-oriented multi-criteria decision-making method

Sustainability has practically become a mandatory concept to be considered in every decision, and multiple decision-making methods have been adapted to take it into account. Among them, sustainability centred methods are also known as sustainability assessments, which provides sustainability indexes to select and prioritize alternatives. One of these most recent presented techniques is MIVES, a multi attribute utility theory multi-criteria decision-making value function-based method initially developed to introduce environmental and social indicators in civil engineering design decisions and later adapted for general evaluation and prioritization of homogenous and heterogeneous alternatives. Over the last decade, it has been widely studied and applied to specific situations, but a MIVES summary is currently lacking. Therefore, in this paper MIVES literature is reviewed with a deep bibliometric analysis carried out to provide on multiple data about MIVES state-of-the-art. Furthermore, a thematic clusters categorisation is done to reveal the usefulness of MIVES as design and decision-making tool, cataloguing the wide applications of MIVES as sustainability index. Finally, a MIVES characteristics discussion is carried out to help researchers deepen their knowledge towards the method and highlight potential future research pathways.The first author acknowledges the Goverment of Spain: Ministry of Education, Culture and Sports [grant number FPU18/01471]. The second and last author wishes to recognize the support from Serra Hunter programme. Finally, this work was supported by Catalan agency AGAUR trough their research groups support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

A theoretical look at ELECTRE TRI-nB and related sorting models

ELECTRE TRI is a set of methods designed to sort alternatives evaluated on several attributes into ordered categories. The original ELECTRE TRI-B method uses one limiting profile per category. A more recent method, ELECTRE TRI-nB, allows one to use several limiting profiles for each category. We investigate the properties of ELECTRE TRI-nB. When the number of limiting profiles used to define each category is not restricted, ELECTRE TRI-nB is easy to characterize axiomatically and is found to be equivalent to several other methods proposed in the literature. We extend this result in various directions.Comment: 40 page