Perceptual maps to aggregate information from decision makers

Understanding different perceptions of human being when using linguistic terms is a crucial issue in human-machine interaction. In this paper, we propose the concept of perceptual maps to model human opinions in a group decision-making context. The proposed approach considers a multi-granular structure using unbalanced hesitant linguistic term sets. An illustrative case is presented in the location decisions made by multinationals enterprises of the energy sector within the European smart city context.This research was supported partly by the INVITE research project (TIN2016- 80049-C2-1-R and TIN2016-80049-C2-2-R), funded by the Spanish Ministry of Science and Information Technology and the European Union Horizon 2020 Research and Innovation Programme, under the grant agreement No 731297.Postprint (published version

Understanding location decisions of energy multinational enterprises within the European smart cities’ context: An integrated AHP and extended fuzzy linguistic TOPSIS method

Becoming a smart city is one of the top priorities in the urban agenda of many European cities. Among the various strategies in the transition path, local governments seek to bring innovation to their cities by encouraging multinational enterprises to deploy their green energy services and products in their municipalities. Knowing how to attract these enterprises implies that political leaders understand the multi-criteria decision problem that the energy sector enterprises face when deciding whether to expand to one city or another. To this end, the purpose of this study is to design a new manageable and controllable framework oriented to European cities’ public managers, based on the assessment of criteria and sub-criteria governing the strategic location decision made by these enterprises. A decision support framework is developed based on the AHP technique combined with an extended version of the hesitant fuzzy linguistic TOPSIS method. The main results indicate the higher relative importance of government policies, such as degree of transparency or bureaucracy level, as compared to market conditions or economic aspects of the city’s host country. These results can be great assets to current European leaders, they show the feasibility of the method and open up the possibility to replicate the proposed framework to other sectors or geographical areas.The authors acknowledge the support from the European Union “Horizon 2020 Research and Innovation Programme” under the grant agreements No 731297. Also, this research has been partially supported by the INVITE Research Project (TIN2016-80049-C2-1-R and TIN2016-80049-C2-2-R (AEI/FEDER, UE)), funded by the Spanish Ministry of Science and Information Technology.Peer ReviewedPostprint (published version

A hesitant fuzzy perceptual-based approach to model linguistic assessments

Multiple-criteria or multiple-attribute group decision-making is a sub-field of operations research that seek to find a common and representative solution given the preferences elicited by a pre-defined group, over a set of alternatives and with respect to a set of coherent criteria (or attributes). Recently, the modelling of natural language in these processes has captured the attention of many researchers. Most of the evaluations in a group-decision making context are inherently imprecise, incomplete or vague, and therefore, experts feel more comfortable using their language rather than numerical values. The use of hesitant fuzzy linguistic term sets is one of the recent tools that enables the modelling of linguistic assessments in multiple-criteria decision-making. Nonetheless, advances in hesitant linguistic multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. More tools are needed in order to really grasp the differences in the qualitative reasoning processes of each individual. This thesis, firstly, introduces a perceptual-based distance able to capture differences between unbalanced linguistic assessments, which is based on a lattice structure of hesitant fuzzy linguistic terms. Secondly, this distance is used to define a perceptual-based centroid or central opinion which, in turn, is used to define a consensus measure or degree of agreement within the group. Thirdly, with the aim to deal with multi-perceptual group decision-making contexts, where each decision maker has its own qualitative reasoning approach, a perceptual-based transformation function and a projected algebraic structure are defined. The developed tools can deal with different multi-granularity linguistic environments. Two applications are presented to demonstrate the utility, relevancy and feasibility of the methods. On the one hand, a specific perceptual-based classification and ranking method is introduced and applied to a real group decision making problem in an educational setting. This framework is used to classify and rank a set of secondary students according to their degree of entrepreneurial competency, which is based on real data provided by the Andorra Government. On the other hand, an extended fuzzy multi-perceptual linguistic TOPSIS is designed and applied to a real group decision making problem in the context of smart city governance. This perceptual extension is used to assess the criteria governing the strategic decision making process of energy multinational companies when deciding where to expand its sustainable services and products.El multiple-criteria o bé multi-attributte group decision-making (MCGDM / MAGDM) és una branca del camp de OR (operations research) l'objectiu del qual és buscar solucions comunes i representatives donades unes preferències d'un grup d'experts definit, sobre un conjunt d'alternatives i en relació a un conjunt coherent de criteris o atributs. L'objectiu d'aquesta tesis és contribuir específicament en l'àrea lingüística de MCGDM / MAGDM millorant les metodologies i marcs matemàtics existents amb l'objectiu de poder modelar qualsevol tipus de situació de presa de decisions en grup que impliqui multi-granularitat i raonament qualitatiu molt heterogeni entre el grup (ús d'etiquetes lingüístiques no balancejades). En concret, la tesis es basa en l'ús de l'eina dels hesitant fuzzy lingüístic term sets (HFLTSs) que fou introduïda per Rodriguez et al (2012) amb l'objectiu de permetre als experts poder donar opinions i preferències lingüístiques usant el seu llenguatge habitual (i no, números) capturant també la incertesa, ambigüitat i manca d'informació característica en aquest tipus de decisions. La majoria d'estructures matemàtiques existents basades en l'ús de HFLTSs en problemes de MCGDM/MAGDM fan la hipòtesis que tots els experts han d'expressar-se usant el mateix set d'etiquetes lingüístiques i/o bé el pes que cada expert dona a cadascuna de les etiquetes ha de ser el mateix. Aquests estructures no són suficientment flexibles per modelar situacions de GDM de multi-granularitat que també incloguin diversitat de raonament qualitatiu amb etiquetes lingüístiques no balancejades de forma simultània. En primer lloc, la present tesis desenvolupa un nou concepte, el perceptual-map, definit sobre l'estructura algebraica de HFLTSs no balancejats i introdueix una nova distància basada en aquesta mètrica. Aquesta distància és utilitzada per definir un centroide (opinió central) i una mesura de consens per a qualsevol situació de MAGDM que necessiti de l'ús d'un set d'etiquetes lingüístiques no balancejat. En segon lloc, una funció de transformació basada en el perceptual-map es defineix per tal de poder modelar simultàniament situacions lingüístiques amb multi-granularitat i poder així, realitzar operacions en un espai projectat. A nivell pràctic, la tesis presenta dos aplicacions reals per demostrar la utilitat i rellevància de les eines matemàtiques desenvolupades. D'una banda, la tesis introdueix un nou mètode de classificació i rànquing, que és aplicat en l'àmbit de l'educació. El nou mètode és utilitzat per classificar i ranquejar els alumnes de secundària de l'escola Andorrana d'acord amb el seu grau de desenvolupament de la competència emprenedora. D'altra banda, s'ha desenvolupat un nou model de TOPSIS anomenat fuzzy multi-perceptual lingüístic TOPSIS, que s'ha aplicat en el context d'avaluació de smart cities. La nova versió de TOPSIS s'ha aplicat amb èxit per avaluar els criteris que governen la decisió estratègica de localització, en el context de ciutats europees, de les multinacionals del sector energètic.Postprint (published version

A fuzzy decision-aiding approach to implement sustainable marine itineraries

Vies Braves (Sea Swimming Lanes) is a pioneer company in designing, promoting, and revitalizing open water and marine itineraries which are protected by buoys, and dedicated to health, leisure, educa- tional and environmental protection activities. The management team's strategic priority is the implementation of new Vies Braves in new coastal locations. In this paper, we propose a fuzzy decision-aiding ap- proach, using hesitant linguistic term sets to study the viability of new potential coastal locations. The proposed approach considers main eco- nomic, political, environmental and social criteria to nd similarities between each new alternative and existing ones.This research was supported partly by the INVITE research project (TIN201680049-C2-1-R and TIN2016-80049-C2-2-R), funded by the Spanish Ministry of Economy and Competitiveness. In addition, the authors gratefully acknowledge the encouraging support of Mr. Miquel Sunyer, CEO of Vies Braves.Peer ReviewedPostprint (author's final draft

A multi-attribute group decision model based on unbalanced and multi-granular linguistic information: an application to assess entrepreneurial competencies in secondary schools

Advances in multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. New distances between linguistic terms are needed to aggregate opinions and measure consensus among decision makers with different profiles. In this paper, firstly, based on the lattice structure of hesitant fuzzy linguistic terms sets, a perceptual-based distance able to capture differences between unbalanced linguistic assessments is developed. Secondly, a projected algebraic structure is defined to deal with multiperceptual group decision-making contexts where each decision maker has its own qualitative reasoning approach. To this end, a methodology to aggregate unbalanced linguistic information based on different perceptual maps is developed. This methodology can also deal with different multi-granularity linguistic environments. Finally, through an illustrative example based on real data provided by the Andorra Government in a pilot test, the proposed framework is applied to classify and rank a set of secondary students according to their degree of entrepreneurial competency.Peer ReviewedPostprint (published version

Improved ensemble learning for wind turbine main bearing fault diagnosis

The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (anomaly detection model designed at wind park level) algorithms trained only on SCADA data. The normal behavior and the anomalous samples of the wind turbines are identified and several interpretable indicators are proposed based on the predictions of these algorithms, to provide the wind park operators with understandable information with enough time to plan operations ahead and avoid unexpected costs. The stated methodology is validated in a real underproduction wind park composed by 18 wind turbines.Peer ReviewedPostprint (published version

An ensemble learning solution for predicitive manintenance of wind turbines main bearing

A novel and innovative solution addressing wind turbines’ main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets.This research was funded by Centro para el Desarollo Tecnológico Industrial, grant number CDTI-IDI 20191294 and Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), grant number DOCTORADO AGAUR-2017-DI 004.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version