A new type of preference relations: Fuzzy preference relations with self-confidence

Preference relations are very useful to express decision makers’ preferences over alternatives in the process of decision-making. However, multiple self-confidence levels are not considered in existing preference relations. In this study, we propose a new type of preference relations: fuzzy preference relations with self-confidence. A linear programming model is proposed for estimating priority vectors of this new type of preference relations. Finally, two numerical examples are provided to demonstrate the linear programming model, and a comparative analysis is used to show the influence of self-confidence levels on the decision-making results

Group decision-making based on heterogeneous preference relations with self-confidence

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Preference relations are very useful to express decision makers’ preferences over alternatives in the process of group decision-making. However, the multiple self-confidence levels are not considered in existing preference relations. In this study, we define the preference relation with self-confidence by taking multiple self-confidence levels into consideration, and we call it the preference relation with self-confidence. Furthermore, we present a two-stage linear programming model for estimating the collective preference vector for the group decision-making based on heterogeneous preference relations with self-confidence. Finally, numerical examples are used to illustrate the two-stage linear programming model, and a comparative analysis is carried out to show how self-confidence levels influence on the group decision-making results

Are incomplete and self-confident preference relations better in multicriteria decision making? A simulation-based investigation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Incomplete preference relations and self-confident preference relations have been widely used in multicriteria decision-making problems. However, there is no strong evidence, in the current literature, to validate their use in decision-making. This paper reports on the design of two bounded rationality principle based simulation methods, and detailed experimental results, that aim at providing evidence to answer the following two questions: (1) what are the conditions under which incomplete preference relations are better than complete preference relations?; and (2) can self-confident preference relations improve the quality of decisions? The experimental results show that when the decision-maker is of medium rational degree, incomplete preference relations with a degree of incompleteness between 20% and 40% outperform complete preference relations; otherwise, the opposite happens. Furthermore, in most cases the quality of the decision making improves when using self-confident preference relations instead of incomplete preference relations. The paper ends with the presentation of a sensitivity analysis that contributes to the robustness of the experimental conclusions

Confidence-consistency driven group decision making approach with incomplete reciprocal intuitionistic preference relations

This is the reference for the online corrected proof versionIntuitionistic preference relations constitute a flexible and simple representation format of experts’ preference on a set of alternative options, while at the same time allowing to accommodate degrees of hesitation inherent to all decision making processes. In comparison with fuzzy preference relations, the use of intuitionistic fuzzy preference relations in decision making is limited, which is mainly due to the computational complexity associated to using membership degree, non-membership degree and hesitation degree to model experts’ subjective preferences. In this paper, the set of reciprocal intuitionistic fuzzy preference relations and the set of asymmetric fuzzy preference relations are proved to be mathematically isomorphic. This result can be exploited to use methodologies developed for fuzzy preference relations to the case of intuitionistic fuzzy preference relations and, ultimately, to overcome the computation complexity mentioned above and to extend the use of reciprocal intuitionistic fuzzy preference relations in decision making. In particular, in this paper, this isomorphic equivalence is used to address the presence of incomplete reciprocal intuitionistic fuzzy preference relations in decision making by developing a consistency driven estimation procedure via the corresponding equivalent incomplete asymmetric fuzzy preference relation procedure. Additionally, the hesitancy degree of the reciprocal intuitionistic fuzzy preference relation is used to introduce the concept of expert’s confidence from which a group decision making procedure, based on a new aggregation operator that takes into account not only the experts’ consistency but also their confidence degree towards the opinion provided, is proposed

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

© 2017 The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions

An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The reciprocal preference relation (RPR) is a powerful tool to represent decision makers’ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the ‘classical’ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed

Multicriteria methodology for decoupling point placement under production postponement strategy

The accelerated growth of global markets and the increased bargaining power of customers, has generated a highly competitive environment with a lot of risks for manufacturing companies. In response, the literature has shown mass customization and, in particular way, the postponement strategy as new paradigms of production that allows offering simultaneously high levels of flexibility and efficiency to consumers. Regarding this issue, the decoupling point location is positioned as the most important decision in the implementation of postponement. Starting from the importance of this topic and from a review of the state of the art, it was detected the need to develop a methodology to locate the decoupling point, by integrating qualitative and quantitative criteria and that additionally allows the participation of panel of experts. Thereby, this thesis shows the development of a new multi-criteria methodology; which consists of 7 steps that allow locating the decoupling point, in a production system, according to the needs of the system and relying on the experience and knowledge of experts. Additionally, in order to validate the performance of the methodology in real cases, two study cases developed in the companies Herragro S.A. and Muebles Marco Gomez, are presentedResumen: El crecimiento acelerado de los mercados globalizados y el aumento del poder de negociación de los clientes, ha generado un ambiente fuertemente competitivo y con gran cantidad de riesgos para las empresas manufactureras. Como respuesta, la literatura ha mostrado a la personalización masiva y, de forma particular a la estrategia de aplazamiento como nuevos paradigmas de la producción que permiten ofrecer a los clientes de forma simultánea altos niveles de flexibilidad y eficiencia. Frente a este tema la ubicación del punto de desacople se posiciona como la decisión más importante en la implementación del aplazamiento. Partiendo de la importancia de este tópico y de una revisión del estado del arte, se detecta la necesidad de desarrollar una metodología que permita ubicar el punto de desacople integrando criterios cualitativos y cuantitativos y que adicionalmente permita la participación de grupos de expertos. De esta forma, la presente tesis muestra el desarrollo de una nueva metodología multicriterio; la cual está conformada por 7 pasos que permiten ubicar el punto de desacople, en un sistema de producción, acorde con las necesidades del sistema y apoyándose de la experiencia y conocimiento de los expertos. Adicionalmente, y con el objetivo de validar el funcionamiento de la metodología en casos reales, se presentan dos casos de estudio desarrollados en las empresas Herragro S.A y Muebles Marco GómezMaestrí