A method based on TOPSIS and distance measures for hesitant fuzzy multiple attribute decision making

The aim of this paper is to provide a methodology to hesitant fuzzy multiple attribute decision making using technique for order preference by similarity to ideal solution (TOPSIS) and distance measures. Firstly, the inadequacies of the existing hesitant fuzzy TOPSIS method are analyzed in detail. Then, based on the developed hesitant fuzzy ordered weighted averaging weighted aver-aging distance (HFOWAWAD) measure, a modified hesitant fuzzy TOPSIS, called HFOWAWAD-TOPSIS is introduced for hesitant fuzzy multiple attribute decision making problems. Moreover, the advantages and some special cases of the HFOWAWAD-TOPSIS are presented. Finally, a numerical example about energy policy selection is provided to illustrate the practicality and feasibility of the developed approach

Construction of interval-valued fuzzy preference relations from ignorance functions and fuzzy preference relations. Application to decision making

The file attached is this record is the authors pre-print. The publishers version of record can be found by following the DOI link

Risk Evaluation in Failure Mode and Effects Analysis Based on D Numbers Theory

Failure mode and effects analysis (FMEA) is a useful technology for identifying the potential faults or errors in system, and simultaneously preventing them from occurring. In FMEA, risk evaluation is a vital procedure. Many methods are proposed to address this issue but they have some deficiencies, such as the complex calculation and two adjacent evaluation ratings being considered to be mutually exclusive. Aiming at these problems, in this paper, A novel method to risk evaluation based on D numbers theory is proposed. In the proposed method, for one thing, the assessments of each failure mode are aggregated through D numbers theory. For another, the combination usage of risk priority number (RPN) and the risk coefficient newly defined not only achieve less computation complexity compared with other methods, but also overcome the shortcomings of classical RPN. Furthermore, a numerical example is illustrated to demonstrate the effectiveness and superiority of the proposed method

Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

There has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

Représentation et combinaison d'informations incertaines : nouveaux résultats avec applications aux études de sûreté nucléaires

It often happens that the value of some parameters or variables of a system are imperfectly known, either because of the variability of the modelled phenomena, or because the availableinformation is imprecise or incomplete. Classical probability theory is usually used to treat these uncertainties. However, recent years have witnessed the appearance of arguments pointing to the conclusion that classical probabilities are inadequate to handle imprecise or incomplete information. Other frameworks have thus been proposed to address this problem: the three main are probability sets, random sets and possibility theory. There are many open questions concerning uncertainty treatment within these frameworks. More precisely, it is necessary to build bridges between these three frameworks to advance toward a unified handlingof uncertainty. Also, there is a need of practical methods to treat information, as using these framerowks can be computationally costly. In this work, we propose some answers to these two needs for a set of commonly encountered problems. In particular, we focus on the problems of:- Uncertainty representation- Fusion and evluation of multiple source information- Independence modellingThe aim being to give tools (both of theoretical and practical nature) to treat uncertainty. Some tools are then applied to some problems related to nuclear safety issues.Souvent, les valeurs de certains paramètres ou variables d'un système ne sont connues que de façon imparfaite, soit du fait de la variabilité des phénomènes physiques que l'on cherche à représenter,soit parce que l'information dont on dispose est imprécise, incomplète ou pas complètement fiable.Usuellement, cette incertitude est traitée par la théorie classique des probabilités. Cependant, ces dernières années ont vu apparaître des arguments indiquant que les probabilités classiques sont inadéquates lorsqu'il faut représenter l'imprécision présente dans l'information. Des cadres complémentaires aux probabilités classiques ont donc été proposés pour remédier à ce problème : il s'agit, principalement, des ensembles de probabilités, des ensembles aléatoires et des possibilités. Beaucoup de questions concernant le traitement des incertitudes dans ces trois cadres restent ouvertes. En particulier, il est nécessaire d'unifier ces approches et de comprendre les liens existants entre elles, et de proposer des méthodes de traitement permettant d'utiliser ces approches parfois cher en temps de calcul. Dans ce travail, nous nous proposons d'apporter des réponses à ces deux besoins pour une série de problème de traitement de l'incertain rencontré en analyse de sûreté. En particulier, nous nous concentrons sur les problèmes suivants :- Représentation des incertitudes- Fusion/évaluation de données venant de sources multiples- Modélisation de l'indépendanceL'objectif étant de fournir des outils, à la fois théoriques et pratiques, de traitement d'incertitude. Certains de ces outils sont ensuite appliqués à des problèmes rencontrés en sûreté nucléaire

Une approche systémique unifiée pour l’optimisation durable des systèmes socio-environnementaux: Ingénierie des systèmes de décision en univers incertain

Nowadays, the sustainability of human activities is a major worldwide concern. The challenge is to evaluate such activities not only in terms of efficiency and productivity, but also in terms of their economic, social, environmental, etc. durability. For this, the experts of these areas need to work collaboratively. In this context, human societies are facing several major challenges such as: (1) process a large amount of information whose volume increases exponentially (“big data”), (2) live in a both dynamic and imperfect real world, (3) predict and assess future states of its activities.The researches we have conducted in this thesis contribute in particular to the domain of decision systems engineering under uncertainty. We have chosen the field of general socio-environmental systems as subject of study, particularly the multidisciplinary field of agriculture. We propose a systemic approach for the sustainable optimization of socio-environmental systems: (1) the meta-modeling of socio-environmental systems, (2) the generic representation of data imperfection flowing in such systems, associated to a decision model in uncertain environment and finally (3) the simulation and the assessment of such systems in dynamic environment for the purpose of decision making by experts which we have illustrated by both a service-oriented architecture model and case studies applied to the agriculture domain.De nos jours, la durabilité des activités humaines devient une préoccupation majeure dans le monde entier. Il s’agit d’évaluer ces activités non seulement en matière d’efficacité et de productivité, mais aussi en ce qui concerne leurs durabilités économique, sociale, environnementale, etc. Pour ce faire, les experts de ces différents domaines doivent travailler en collaboration. Dans ce contexte, les sociétés humaines sont confrontées à plusieurs défis majeurs qui sont les suivants : (1) traiter de grandes quantités d’informations (« big data »), (2) évoluer dans un monde réel dynamique et imparfait, (3) prévoir et évaluer les états futurs de ses activités.Les recherches que nous avons menées dans cette thèse contribuent plus particulièrement au domaine de l’ingénierie des systèmes de décision en univers incertain. Nous prenons comme objet d'étude général le domaine des systèmes socio-environnementaux, et plus particulièrement le domaine pluridisciplinaire de l’agriculture. Nous proposons une approche systémique pour l’optimisation durable des systèmes socio-environnementaux : (1) la méta-modélisation des systèmes socio-environnementaux, (2) la représentation générique de l’imperfection des informations qui circulent dans ces systèmes, associée à un modèle de décision en contexte incertain et enfin (3) la simulation et l’évaluation de ces systèmes en environnement dynamique en vue de prises de décisions par des experts, que nous avons illustrée par un modèle d’architecture orientée service ainsi que des études de cas appliquées au domaine de l’agriculture