A robust fuzzy possibilistic AHP approach for partner selection in international strategic alliance

The international strategic alliance is an inevitable solution for making competitive advantage and reducing the risk in today’s business environment. Partner selection is an important part in success of partnerships, and meanwhile it is a complicated decision because of various dimensions of the problem and inherent conflicts of stockholders. The purpose of this paper is to provide a practical approach to the problem of partner selection in international strategic alliances, which fulfills the gap between theories of inter-organizational relationships and quantitative models. Thus, a novel Robust Fuzzy Possibilistic AHP approach is proposed for combining the benefits of two complementary theories of inter-organizational relationships named, (1) Resource-based view, and (2) Transaction-cost theory and considering Fit theory as the perquisite of alliance success. The Robust Fuzzy Possibilistic AHP approach is a noveldevelopment of Interval-AHP technique employing robust formulation; aimed at handling the ambiguity of the problem and let the use of intervals as pairwise judgments. The proposed approach was compared with existing approaches, and the results show that it provides the best quality solutions in terms of minimum error degree. Moreover, the framework implemented in a case study and its applicability were discussed

Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels

A possibilistic approach to latent structure analysis for symmetric fuzzy data.

In many situations the available amount of data is huge and can be intractable. When the data set is single valued, latent structure models are recognized techniques, which provide a useful compression of the information. This is done by considering a regression model between observed and unobserved (latent) fuzzy variables. In this paper, an extension of latent structure analysis to deal with fuzzy data is proposed. Our extension follows the possibilistic approach, widely used both in the cluster and regression frameworks. In this case, the possibilistic approach involves the formulation of a latent structure analysis for fuzzy data by optimization. Specifically, a non-linear programming problem in which the fuzziness of the model is minimized is introduced. In order to show how our model works, the results of two applications are given.Latent structure analysis, symmetric fuzzy data set, possibilistic approach.

Análisis de armónicos variando en el tiempo en sistemas eléctricos de potencia con parques eólicos, a través de la teoría de la posibilidad

This paper focuses on the analysis of the connection of wind farms to the electric power system and their impact on the harmonic load-flow. A possibilistic harmonic load-flow methodology, previously developed by the authors, allows for modeling uncertainties related to linear and nonlinear load variations. On the other hand, it is well known that some types of wind turbines also produce harmonics, in fact, time-varying harmonics. The purpose of this paper is to present an improvement of the former method, in order to include the uncertainties due to the wind speed variations as an input related with power generated by the turbines. Simulations to test the proposal are performed in the IEEE 14-bus standard test system for harmonic analysis, but replacing the generator, at bus two, by a wind farm composed by ten FPC type wind turbines.En este trabajo se analiza el impacto de la conexión de parques eólicos, en el flujo de cargas armónicas en un sistema de potencia. Algunos generadores eólicos producen armónicos debido a la electrónica de potencia que utilizan para su vinculación con la red. Estos armónicos son variables en el tiempo ya que se relacionan con las variaciones en la velocidad del viento. El propósito de este trabajo es presentar una mejora a la metodología para el cálculo de incertidumbre en el flujo de cargas armónicas, a través de la teoría de la posibilidad, la cual fue previamente desarrollada por los autores. La mejora consiste en incluir la incertidumbre debida a las variaciones de la velocidad del viento. Para probar la metodología, se realizan simulaciones en el sistema de prueba de 14 barras de la IEEE, conectando en una de las barras un parque eólico compuesto por diez turbinas del tipo FPC. Los resultados obtenidos muestran que la incertidumbre en la velocidad del viento tiene un efecto considerable en las incertidumbres asociadas a las magnitudes de las tensiones armónicas calculadas.Fil: Romero Quete, Andrés Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Suvire, Gaston Orlando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Zini, Humberto Cassiano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Ratta, Giuseppe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Induction of Interpretable Possibilistic Logic Theories from Relational Data

The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which make them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.Comment: Longer version of a paper appearing in IJCAI 201

Possibilistic networks parameter learning: Preliminary empirical comparison

International audienceLike Bayesian networks, possibilistic ones compactly encode joint uncertainty representations over a set of variables. Learning possibilistic networks from data in general and from imperfect or scarce data in particular, has not received enough attention. Indeed, only few works deal with learning the structure and the parameters of a possibilistic network from a dataset. This paper provides a preliminary comparative empirical evaluation of two approaches for learning the parameters of a possibilistic network from empirical data. The first method is a possibilistic approach while the second one first learns imprecise probability measures then transforms them into possibility distributions by means of probability-possibility transformations. The comparative evaluation focuses on learning belief networks on datasets with missing data and scarce datasets