Professor Enrique Herrera-Viedma

Job: Professor of Soft Computing and Intelligent Information Systems

Faculty: Technology

School/department: School of Computer Science and Informatics

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: N/A

E: enrique.herrera-viedma@dmu.ac.uk

W: https://www.dmu.ac.uk/

 

Research group affiliations

  1. DIGITS – De Montfort University Interdisciplinary Group in Intelligent Transport Systems
  2. Centre for Computational Intelligence

Publications and outputs 

  • Consensus Reaching with Time Constraints and Minimum Adjustments in Group with Bounded Confidence Effects
    Consensus Reaching with Time Constraints and Minimum Adjustments in Group with Bounded Confidence Effects Liang, Haiming; Dong, Yucheng; Ureña, Raquel; Chiclana, Francisco; Herrera-Viedma, Enrique; Ding, Zhaogang In the bounded confidence model it is widely known that individuals rely on the opinions of their close friends or people with similar interests. Meanwhile, the decision maker always hopes that the opinions of individuals can reach a consensus in a required time. Therefore, with this idea in mind, this paper develops a consensus reaching model with time constraints and minimum adjustments in a group with bounded confidence effects. In the proposed consensus approach, the minimum adjustments rule is used to modify the initial opinions of individuals with bounded confidence, which can further influence the opinion evolutions of individuals to reach a consensus in a required time. The properties of the model are studied, and detailed numerical examples and comparative simulation analysis are provided to justify its feasibility. 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.
  • Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations
    Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations Xu, Yejun; Li, Mengqi; Cabrerizo, Francisco Javier; Chiclana, Francisco; Herrera-Viedma, Enrique Consistency, multiplicative and ordinal, of fuzzy preference relations (FPRs) is investigated. The geometric consistency index (GCI) approximated thresholds are extended to measure the degree of consistency for an FPR. For inconsistent FPRs, two algorithms are devised (1) to find the multiplicative inconsistent elements, and (2) to detect the ordinal inconsistent elements. An integrated algorithm is proposed to improve simultaneously the ordinal and multiplicative consistencies. Some examples, comparative analysis, and simulation experiments are provided to demonstrate the effectiveness of the proposed methods. 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.
  • Are incomplete and self-confident preference relations better in multicriteria decision making? A simulation-based investigation
    Are incomplete and self-confident preference relations better in multicriteria decision making? A simulation-based investigation Dong, Yucheng; Liu, Wenqi; Chiclana, Francisco; Kou, Gang; Herrera-Viedma, Enrique; Herrera 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. 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 evolution with deceptive interactions and heterogeneous trust in bounded confidence model: A simulation analysis.
    Preference evolution with deceptive interactions and heterogeneous trust in bounded confidence model: A simulation analysis. Dong, Yucheng; Yuxiang, Fan; Haiming, Liang; Chiclana, Francisco; Herrera-Viedma, Enrique The bounded confidence model is a popular tool to model the evolution of preferences and knowledge in opinion dynamics. In the bounded confidence model, it is assumed that all agents are honest to express their preferences and knowledge. However, in real-life opinion dynamics, agents often hide their true preferences, and express different preferences to different people. In this paper, we propose the evolution of preferences with deceptive interactions and heterogeneous trust in bounded confidence model, in which some agents will express three types of preferences: true preferences, communicated preferences and public preferences. In the proposed model, the communication regimes of the agents are established. Based on the established communication regimes, the true preferences, communicated preferences and public preferences of the agents are updated. Furthermore, we use an agent-based simulation to unfold the influences of the deceptive interactions and heterogeneous trust on the evolutions of preferences. 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.
  • Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets
    Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets Urena, Raquel; Kou, Gang; Wu, Jian; Chiclana, Francisco; Herrera-Viedma, Enrique Nowadays in the social network based decision making processes, as the ones involved in e-commerce and e-democracy, multiple users with di erent backgrounds may take part and diverse alternatives might be involved. This diversity enriches the process but at the same time increases the uncertainty in the opinions. This uncertainty can be considered from two di erent perspectives: (i) the uncertainty in the meaning of the words given as preferences, that is motivated by the heterogeneity of the decision makers, (ii) the uncertainty inherent to any decision making process that may lead to an expert not being able to provide all their judgments. The main objective of this contribution is to address these two type of uncertainty. To do so the following approaches are proposed: Firstly, in order to capture, process and keep the uncertainty in the meaning of the linguistic assumption the Interval Type 2 Fuzzy Sets are introduced as a way to model the experts linguistic judgments. Secondly, a measure of the coherence of the information provided by each decision maker is proposed. Finally, a consistency based completion approach is introduced to deal with the uncertainty presented in the expert judgments. The proposed approach is tested in an e-democracy decision making scenario. 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.
  • A Self-Management Mechanism to Manage Non-cooperative Behaviors in LGDM-Based Supply Chain Risk Mitigation
    A Self-Management Mechanism to Manage Non-cooperative Behaviors in LGDM-Based Supply Chain Risk Mitigation Zhao, Sihai; Dong, Yucheng; Zhang, Hengjie; Chiclana, Francisco; Herrera-Viedma, Enrique Large-scale group decision making (LGDM) is becoming more and more common, and how to assure the security and quality of the decision making process has become a hot topic. Supply chain risk mitigation is a complex LGDM problem involving in many stakeholders. In the decision making process, a group of experts aims at reaching a consensus among alternatives in which non-cooperative behaviors often appear. Some experts might designedly form a small alliance and change their preferences in a direction against consensus with the aim to foster the alliance’s own interests. In this study, we present a novel large-scale consensus reaching framework based on a self- management mechanism to manage non-cooperative behaviors. In the proposed framework, experts are classified into different subgroups using a clustering method, and they provide their evaluation information, i.e., the multi-criteria mutual evaluation matrices (MCMEMs), regarding the obtained subgroups based on their performance. The subgroups’ weights are generated dynamically from the MCMEMs, which are in turn used to update experts’ weights. This mechanism allows penalizing the weights of the experts with non-cooperative behaviors. Detailed comparison analysis is presented to verify the validity of the proposed consensus framework for supply chain risk mitigation. 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.
  • A variance-based consensus degree in group decision making problems
    A variance-based consensus degree in group decision making problems del Moral, Maria Jose; Chiclana, Francisco; Tapia, Juan Miguel; Herrera-Viedma, Enrique The variance is a well-known statistical measure and is frequently used for the calculation of variability. This concept can be used to obtain the degree of agreement in groups that have to make decisions. In this study, we propose the use of a variance derivative as an alternative for the calculation of the degree of consensus for Group Decision Making problems with fuzzy preference relations. As revealed by a subsequent comparative study, the values obtained by this new method are comparable to the values obtained by means of frequently used methods that employ distance functions and aggregation operators, while it turns out to be a simpler application method.
  • Guest Editorial: Intelligent Decision Making and Consensus Under Uncertainty in Inconsistent and Dynamic Environments
    Guest Editorial: Intelligent Decision Making and Consensus Under Uncertainty in Inconsistent and Dynamic Environments Herrera-Viedma, Enrique; Chiclana, Francisco; Dong, Yucheng; Loia, Vincenzo; Kou, Gang; Fujita, Hamido
  • An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions
    An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions Cong-Cong, Li; Dong, Yucheng; Xu, Yejun; Chiclana, Francisco; Herrera-Viedma, Enrique; Herrera, Francisco 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. 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.
  • A review on trust propagation and opinion dynamics in social networks and group decision making frameworks
    A review on trust propagation and opinion dynamics in social networks and group decision making frameworks Urena, Raquel; Kou, Gang; Dong, Yucheng; Chiclana, Francisco; Herrera-Viedma, Enrique On-line platforms foster the communication capabilities of the Internet to develop large-scale influence networks in which the quality of the interactions can be evaluated based on trust and reputation. So far, this technology is well known for building trust and harnessing cooperation in on-line marketplaces, such as Amazon (www.amazon.com) and eBay (www.ebay.es). However, these mechanisms are poised to have a broader impact on a wide range of scenarios, from large scale decision making procedures, such as the ones implied in e-democracy, to trust based recommendations on e-health context or influence and performance assessment in e-marketing and e-learning systems. This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose. To do so, it discusses trust, reputation and influence which are important measures in networked based communication mechanisms to support the worthiness of information, products, services opinions and recommendations. The existent mechanisms to estimate and propagate trust and reputation, in distributed networked scenarios, and how these measures can be integrated in decision making to reach consensus among the agents are analysed. Furthermore, it also provides an overview of the relevant work in opinion dynamics and influence assessment, as part of social networks. Finally, it identifies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommendation mechanisms in complex social networks scenarios with uncertain knowledge, like the mentioned in e-health and e-marketing frameworks. 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.

Click here to view a full listing of Enrique Herrera-Viedma's publications and outputs.

Research interests/expertise

  • Fuzzy preference modelling
  • Decision making problems with heterogeneous fuzzy information
  • Decision support systems, the consensus reaching process
  • Recommender systems, social networks
  • Modelling situations with missing/incomplete information
  • Rationality/consistency
  • Intelligent mobility and aggregation of information
  • Information retrieval
  • Bibliometrics analysis
  • Digital libraries.

Honours and awards

  • Best conference paper Awards:

S. Alonso, I.J. Pérez, E. Herrera-Viedma, F.J. Cabrerizo, Consensus with Linguistic Preferences in Web 2.0 Communities. 9th International Conference on Intelligent Systems Design and Applications (ISDA09), Pisa (Italy), 809-814, November 30 - December 2, 2009.

  • 2011 IEEE CIS TFS Outstanding Paper Award

F. Herrera, E. Herrera-Viedma, L. Martínez. A Fuzzy Linguistic Methodology To Deal With Unbalanced Linguistic Term Sets. IEEE Transactions on Fuzzy Systems 16:2 (2008) 354-370.

Membership of professional associations and societies

  •     Member of the European Society for Fuzzy Logic and Technology (EUSFLAT)
  •     IEEE member from 2012.

Conference attendance

PC membership

IFSA2009 / EUSFLAT09
FLINS2010 
FCTA 2013
AGOP2013
IFSA-NAFIPS 2013
ISDA'09 
ISKE2009 
ISKE2012
ITQM 2013
Multiconference CAEPIA 2013
UKCI2013
ICNC-FSKD 2013 

  • 28th International Conference on Artificial Intelligence. Application Stream (2008)
  • IEEE International Conference on Fuzzy Systems (2008)
  • IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (2009, 2011, 2013)
  • International Conference on Electronic Commerce and Web Technologies (2008)
  • Fuzzy Logic and Intelligent Technologies in Nuclear Science FLINS (2008, 2010)
  • IEEE Globecom Computer and Communications Network Security Symposium (2008,2009)
  • UK Workshop on Computational Intelligence (2008)
  • SGAI International Conference on Artificial Intelligence (2008)
  • EUROFUSE Workshop on Preference Modelling and Applications (2009, 2011)
  • Global Congress on Intelligent Systems (2009, 2010)
  • International Conference on Intelligent Systems and Knowledge Engineering (2009,2011)
  • International Conference on Fuzzy Computation (2009)
  • International Conference on Fuzzy Systems and Knowledge Discovery (2010)
  • International Symposium on Intelligent Decision Technologies (2010)
  • 2nd KES International Symposium on Intelligent Decision Technologies IDT'10 (2010)
  • 2011 International Conference onIntelligent Systems and Knowledge Engineering (2011)
  • 2009 International Fuzzy Systems Association WORLD CONGRESS (2009)
  • 2009 European Society for Fuzzy Logic and Technology CONFERENCE   (2009)
  • International Conference on Intelligent Systems Design and Applications (ISDA’09).

Externally funded research grants information

Intelligent systems for decisión making in heterogeneous contexts
Reference: Andalusian Excellence Project.TIC-5991
Period: November 2010 – June 2014

FUZZY-LING II: Fuzzy linguistic modelling of preferences: Applications in Information Retrieval and Group Decision Making.
Reference: Education Ministery. TIN2010-17876
Period: January 2011 – December 2013

Developing the fuzzy linguistic modelling and its use in web applications.
Reference: TIC-05299
Period: November 2009 – June 2013

FUZZY-LING: New fuzzy linguistic approaches for preference modelling:Applications on Information Retrieval and Decision Making
Project: Education Ministery. TIN2007-61079
Period: June 2007 - June 2011

Professional esteem indicators

Associate Editor

  • KNOWLEDGE BASED SYSTEMS
    From 2010
  • INFORMATION SCIENCES
    From 2010
  • SOFT COMPUTING 
    From 2012
  • JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 
    From 2012
  • FUZZY OPTIMIZATION & DECISION MAKING
    From 2013
  • IEEE Transactions on Systems, Man, and Cybernetics: Systems
    From 2013

Member of the Editorial Board:

  • FUZZY SETS AND SYSTEMS
    From 2007
  • SOFT COMPUTING 
    From 2007
  • INT. J. OF INFORMATION TECHNOLOGY AND DECISION MAKING
    From 2008
  • INT. J. OF COMPUTATIONAL INTELLIGENCE SYSTEMS
    From 2010

Journal Refereeing information

  • Annals of Operations Research
  • Applied Mathematical Modelling
  • Computers & Industrial Engineering
  • Data & Knowledge Engineering (DKE) Journal
  • European Journal of Operational Research
  • Fuzzy Sets and Systems
  • Group Decision and Negotiation
  • IEEE Transactions on Fuzzy Systems
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Systems, Man and Cybernetics, Part A
  • IEEE Transactions on Systems, Man and Cybernetics, Part B
  • IEEE Transactions on Systems, Man and Cybernetics, Part C
  • IMA Journal of Management Mathematics
  • Information Fusion
  • Information Sciences
  • International Journal of Approximate Reasoning
  • International Journal Artificial Intelligence in Medicine
  • International Journal of Computational Intelligence Research
  • International Journal of Information Technology & Decision Making
  • Journal of Intelligent and Fuzzy Systems
  • International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems(IJUFKS)
  • Journal of Environmental Management
  • Journal of Intelligent and Fuzzy Systems
  • Journal of Multiple-Valued Logic and Soft Computing
  • Journal of Operational Research Society
  • Journal of Systems Science and Systems Engineering
  • Knowledge-Based Systems 
  • Omega
  • Soft Computing
  • The Open Cybernetics and Systemics Journal.

Other Reviewing Activities

  • Switzerland National Council for Development and Innovation (2012)
  • Portuguese Foundation for Science and Technology (FCT) (2012 and 2013).

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