Juan Antonio Morente-Molinera

Managing incomplete preference relations in decision making

In decision making, situations where all experts are able to efficiently express their preferences over all the available options are the exception rather than the rule. Indeed, the above scenario requires all experts to possess a precise or sufficient level of knowledge of the whole problem to tackle, including the ability to discriminate the degree up to which some options are better than others. These assumptions can be seen unrealistic in many decision making situations, especially those involving a large number of alternatives to choose from and/or conflicting and dynamic sources of information. Some methodologies widely adopted in these situations are to discard or to rate more negatively those experts that provide preferences with missing values. However, incomplete information is not equivalent to low quality information, and consequently these methodologies could lead to biased or even bad solutions since useful information might not being taken properly into account in the decision process. Therefore, alternative approaches to manage incomplete preference relations that estimates the missing information in decision making are desirable and possible. This paper presents and analyses methods and processes developed on this area towards the estimation of missing preferences in decision making, and highlights some areas for future research.

On multi-granular fuzzy linguistic modeling in group decision making problems: a systematic review and future trends

The multi-granular fuzzy linguistic modeling allows the use of several linguistic term sets in fuzzy linguistic modeling. This is quite useful when the problem involves several people with different knowledge levels since they could describe each item with different precision and they could need more than one linguistic term set. Multi-granular fuzzy linguistic modeling has been frequently used in group decision making field due to its capability of allowing each expert to express his/her preferences using his/her own linguistic term set. The aim of this research is to provide insights about the evolution of multi-granular fuzzy linguistic modeling approaches during the last years and discuss their drawbacks and advantages. A systematic literature review is proposed to achieve this goal. Additionally, some possible approaches that could improve the current multi-granular linguistic methodologies are presented.

A linguistic mobile Decision Support System based on fuzzy ontology to facilitate knowledge mobilization

The current development of the Semantic Web has created an increasing demand for methods and systems that can make use of imprecise information. As the amounts of data collected constantly grows, it will not be feasible to overlook imprecise data. We show that a combination of mobile technology and fuzzy ontology with group decision making support methods will facilitate a mobilization of knowledge, offering users a possibility to get decision making support through their mobile devices regardless of the context and location. In this paper, as an illustration and verification, a web platform and an Android application have been developed to help users to choose a suitable wine for different types of dinners. Web and Android platform applications employing a Fuzzy Wine Ontology have been implemented for imprecise information.We show that imprecise expert knowledge can be activated and managed using mobile devices.Knowledge mobilization will make it possible for users to get decision support regardless of where they are.We show how to use imprecise data with a combination of decision support algorithms and fuzzy ontology.The adoption of mobile devices will change the way decisions are made in everyday life, in business and in daily routines.

Creating knowledge databases for storing and sharing people knowledge automatically using group decision making and fuzzy ontologies

Over the last decade, the Internet has undergone a profound change. Thanks to Web 2.0 technologies, the Internet has become a platform where everybody can participate and provide their own personal information and experiences. Ontologies were designed in an effort to sort and categorize all sorts of information. In this paper, an automatized method for retrieving the subjective Internet users information and creating ontologies is described. Thanks to this method, it is possible to automatically create knowledge databases using the common knowledge of a large amount of people. Using these databases, anybody can consult and benefit from the retrieved information. Group decision making methods are used to extract users information and fuzzy ontologies are employed to store the collected knowledge.

Building and managing fuzzy ontologies with heterogeneous linguistic information

Fuzzy ontologies allow the modeling of real world environments using fuzzy sets mathematical environment and linguistic modeling. Therefore, fuzzy ontologies become really useful when the information that is worked with is imprecise. This happens a lot in real world environments because humans are more used to think using imprecise nature words instead of numbers. Furthermore, there is a high amount of concepts that, because of their own nature, cannot be measured numerically. Moreover, due to the fact that linguistic information is extracted from different sources and is represented using different linguistic term sets, to deal with it can be problematic. In this paper, three different novel approaches that can help us to build and manage fuzzy ontologies using heterogeneous linguistic information are proposed. Advantages and drawbacks of all of the new proposed approaches are exposed. Thanks to the use of multi-granular fuzzy linguistic methods, information can be expressed using different linguistic term sets. Multi-granular fuzzy linguistic methods can also allow users to choose the linguistic term sets that they prefer to formulate their queries. In such a way, user-computer communication is improved since users feel more comfortable when using the system.

GDM-R

With the incorporation of web 2.0 frameworks the complexity of decision making situations has exponentially increased, involving in many cases a huge number of decision makers, and many different alternatives. In the literature we can find a great variety of methodologies to assist multi-person decision making. However these classical approaches are not suitable to deal with such complexity since there are no tools able to carry out automatically the decision making processes, providing graphical information about its evolution.The main objective of this contribution is to present an open source framework fully developed in R to carry out consensus guided decision making processes using fuzzy preference relations and providing mechanism to deal with missing information. The system includes tools to visualize the evolution of the decision making process and presents various operation modes, including a test operation one which automatically creates a customized decision scenario to validate, test and compare among various decision making approaches.

Parallel 3D-TLM algorithm for simulation of the Earth-ionosphere cavity

A parallel 3D algorithm for solving time-domain electromagnetic problems with arbitrary geometries is presented. The technique employed is the Transmission Line Modeling (TLM) method implemented in Shared Memory (SM) environments. The benchmarking performed reveals that the maximum speedup depends on the memory size of the problem as well as multiple hardware factors, like the disposition of CPUs, cache, or memory. A maximum speedup of 15 has been measured for the largest problem. In certain circumstances of low memory requirements, superlinear speedup is achieved using our algorithm. The model is employed to model the Earth-ionosphere cavity, thus enabling a study of the natural electromagnetic phenomena that occur in it. The algorithm allows complete 3D simulations of the cavity with a resolution of 10km, within a reasonable timescale.

Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods

Abstract Classic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, we focus on solving this issue by carrying out multi-criteria group decision making methods using a different novel approach. Concretely, fuzzy ontologies reasoning procedures are used in order to automatically obtain the alternatives ranking classification. Thanks to our novel methodology, experts only need to provide the importance of a small set of criteria values making it possible for experts to perform multi-criteria group decision making procedures that have a high amount of alternatives without having to directly deal with them. Furthermore, in order to allow experts to provide their preferences in a comfortable way, multi-granular fuzzy linguistic modelling is used in order to allow each expert to choose the linguistic label set that better fits him/her.

Reaching Consensus in Digital Libraries: A Linguistic Approach☆

Abstract Libraries are recently changing their classical role of providing stored information into new virtual communities, which involve large number of users sharing real time information. Despite of those good features, there is still a necessity of developing tools to help users to reach decisions with a high level of consensus in those new virtual environments. In this contribution we present a new consensus reaching tool with linguistic preferences designed to minimize the main problems that this kind of organization presents (low and intermittent participation rates, difficulty of establishing trust relations and so on) while incorporating the benefits that a new digital library offers (rich and diverse knowledge due to a large number of users, real-time communication and so on). The tool incorporates some delegation and feedback mechanisms to improve the speed of the process and its convergence towards a consensual solution.

On incomplete fuzzy and multiplicative preference relations in multi-person decision making

Abstract Rapid changes in the business environment such us the globalization as well as the increasing necessity to make crucial decisions involving a huge range of alternatives in short period of time or even in real time have made that computerized group decision support systems become very useful tools. However in the majority of the cases the panel of experts cannot provide all the information about their preferences due to different reasons such as lack of knowledge, time etc. Therefore different approaches have been presented to deal with the missing preferences in group decision making contexts. In this paper we review and analyse the state-of-the-art research efforts carried out on this topic for incomplete fuzzy preference relations and multiplicative preference relations.