DORA :: Browsing by Author "Chiclana, Francisco"

Browsing by Author "Chiclana, Francisco"

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A bilateral negotiation mechanism by dynamic harmony threshold for group consensus decision making
(Elsevier, 2024-03-16) Cao, Mingshuo; Chiclana, Francisco; Liu, Yujia; Wu, Jian; Herrera-Viedma, Enrique
This article proposes a framework for bilateral negotiation mechanism to deal the case the concordant decision-makers (DMs) coalition cannot be constructed, which resolves the limitations of the existing group decision making methods. Bilateral negotiation means a process in which any two involved DMs change their own opinions based on each other’s opinions, avoiding the formation of group coalitions and the coercion of individual DMs. It can not only improve group consensus by interaction between individual DMs, but also considers the limited compromise behavior of DMs in the consensus bargaining process. The key contributions of this article contain: (1) It investigates the concept of ‘harmony threshold’ by combining the consensus levels of individual DMs and the number of group members to explain the limited compromise behavior of DMs. (2) it proposes a novel bilateral negotiation consensus mechanism with personalized compromise behavior with the group consensus threshold as the objective function and personalized harmony thresholds as constraints to help any two discording DMs partly to adopt each other’s opinions. And (3) It develops the ranking difference level (RDL) to measure the deviation degree between the final ranking of alternatives and all the DMs’ original rankings of alternatives. The research found that the proposed mechanism can reduce consensus cost by 40% and ranking difference by 5%.
Open Access
A Confidence and Conflict-based Consensus Reaching Process for Large-scale Group Decision Making Problems with Intuitionistic Fuzzy Representations
(IEEE, 2024-03-07) Ding, Ru-Xi; Yang, Bing; Yang, Guo-Rui; Li, Meng-Nan; Wang, Xueqing; Chiclana, Francisco
With the development of social democracy, the public begin to participate in large-scale group decision making (LSGDM) events that have a significant impact on their personal interests. However, the participation of the public with insufficient expertise will cause much hesitancy in the evaluations of decision makers (DMs), which can be captured by intuitionistic fuzzy sets. Meanwhile, due to the increment in the number of DMs, the cost of consensus reaching processes (CRPs), which are utilized to help DMs reach a consensus, is getting higher and higher. In order to improve the efficiency of the CRP, this paper presents a confidence and conflict-based consensus reaching process (CC-CRP) for LSGDM events with intuitionistic fuzzy representations. In the proposed model, according to the hesitancy of the DMs’ intuitionistic fuzzy evaluations, an objective method is firstly developed to calculate the confidence level of DMs that does not require any extra information. Then, a three-dimension clustering method is designed by considering the type of conflict, the degree of conflict, and the confidence level of DMs. After this, an efficiency rate of modification is defined to select DMs who will be persuaded first to adjust their evaluations with recommendation plans generated by a specific optimal method. Finally, according to the clustering process results, different CC-CRP management methods will apply to DMs with different attributes. An illustrative example and several experiments are reported to provide evidence that the proposed model is feasible and effective.
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A Framework of Directed Network Based Influence-Trust Fuzzy Group Decision Making
(Springer Nature, 2023-08-17) Kamis, Nor Hanimah; Kilicman, Adem; Kadir, Norhidayah A.; Chiclana, Francisco
Daily life requires individuals or groups of decision-makers to engage in critical decision-making processes. Fuzzy set theory has been integrated into group decision-making (GDM) to address the ambiguity and vagueness of expert preferences. Social Network Group Decision Making (SNGDM) is a newly emerging research area that focuses on the use of social networks to facilitate information exchange and communication among experts in GDM. Moreover, Social Influence Group Decision Making (SIGDM) has been initiated, which considers social influence as a factor that can impact experts’ preferences during interactions or discussions. Studies in this area have proposed innovative measurements of social influence, including the use of trust statements to explicitly influence experts’ opinions. In this study, a new trust index called TrustRank is proposed, which acts as an additional weightage of experts’ importance and is embedded in the influence network measure that represents the strength of the expert’s influence degree. These values are then utilized as the order-inducing variable in the IOWA-based fusion operator to obtain the collective preference and ranking of alternatives. The proposed framework, which is the directed network-based Influence-Trust Fuzzy GDM, is presented along with its implementation, results, and discussion to showcase its applicability.
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A minimum cost-maximum consensus jointly driven feedback mechanism under harmonious structure in social network group decision making
(Elsevier, 2023-10-30) Wang, Sha; Chiclana, Francisco; Chang, Jia Li; Xing, Yumei; Wu, Jian
This article investigates a minimum cost-maximum consensus jointly driven feedback mechanism under a harmonious power structure by twofold group and individual attention recommendations for building social network consensus. Harmonious power structure is first constructed with subgroup-centrality-IOWA operator by (i) extracting subgroup importance rankings through social network analysis, and (ii) minimising group structure conflict to search the harmony weight allocation. Subsequently, a twofold attention recommendation approach that balances group attention and individual attention is proposed to generate feedback recommendations for the feedback recipients. Based on this, optimisation models that minimise individual adjustment cost and maximise group consensus are constructed, jointly driving the feedback mechanism. Finally, a demonstration example is provided to illustrate the efficacy of the proposed model.
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A picture fuzzy set multi criteria decision-making approach to customize hospital recommendations based on patient feedback
(Elsevier, 2024-02-01) Bani-Doumi, Mohammad; Serrano-Guerrero, Jesus; Chiclana, Francisco; Romero, Francisco P.; Olivas, Jose A.
Sentiment analysis techniques have allowed exploiting the information available in millions of opinions conveyed through different Internet services. One example would be the multiple opinions about medical experiences in hospitals available on the website called Careopinion. These opinions usually talk about different medical aspects such as staff, facilities, etc., in a positive, negative, or neutral manner. Nevertheless, there are situations in which the same opinion contains positive, neutral and negative ideas regarding the same aspect. This fact leads to a perception of hesitancy and uncertainty about the opinion. To deal with this issue, this study proposes a picture fuzzy set-based model able to represent this hesitancy in terms of polarity values. To test this model, it has been used to implement a multicriteria decision making-based hospital recommender which considers the patient preferences with respect to the aspects of the hospitals. The proposed approach has been tested using real reviews from 8 hospitals considering diverse patient preferences. The results of all experiments were compared against an ideal ranking computed from the patient reviews using Spearman’s footrule. Furthermore, to assess the effectiveness of the proposal, it has been compared against other state-of-the-art logic-based polarity representation mechanisms. The findings demonstrate that the proposed approach is more effective than the other polarity representation methods by at least 4%, confirming the superiority of the proposed approach to capture and represent sentiments in an accurate manner.
Embargo
A reputation-based trust evaluation model in group decision-making framework
(Elsevier, 2023-10-20) You, Xinli; Hou, Fujun; Chiclana, Francisco
In group decision-making (GDM) problems, experts need to communicate and adjust their opinions in order to achieve consensus on the final decision-making output. Since experts may have conflicting opinions, trust can be critical and an important reference to use in the decision-making process when some experts are required to modify opinions. Recently, decision-making models based on trust and reputation have been investigated intensively. However, these research works usually rely on the constructed social trust network and honesty and fairness of the trust ratings from experts are taken for granted. The objective of this study is to develop a reputation-based trust model for GDM framework to obtain the trust relationship among experts from their direct interaction and word of mouth. First, the paper defines a trust credibility measure to filter out malicious experts before trust assessment, and designs direct trust feedback based on the interaction quality. Then, based on this direct trust feedback, the global reputation model is proposed according to the synthetical performance of received and provided trust feedback, which encourages long-term good behaviour and guarantees trustworthy communications and interactions. The reputation-based trust and direct trust feedback together build trust relationship among experts. Finally, a simulation experimental analysis of the proposed trust and reputation models is carried out to verify their effectiveness in trust and reputation establishment among the experts, even under the presence of malicious ones.
Open Access
A tolerance index based non-cooperative behaviour managing method with minimum cost in social network group decision making
(Elsevier, 2024-06-26) Sun, Qi; Wu, Jian; Chiclana, Francisco; Ji, Feixia
This paper introduces a novel consensus theoretical framework designed to effectively manage non-cooperative behavior in social network group decision making (SNGDM). It addresses the challenge by considering both individuals’ willingness to adjust preferences and the associated costs of achieving consensus. To deal with this issue, the personalized individual semantics (PIS) model is employed to handle original evaluation matrices by converting linguistic terms into numerical values based on experts’ personalized opinions. Subsequently, a tolerance index (TI) is defined to reflect the willingness of experts to adjust their preferences. An improved minimum cost (MC) feedback model based on TI is established. The novelty of the proposed approach is that its integration of individual preference adjustment willingness and consensus efficiency, effectively preventing groupthink. In addition, a maximum group consensus degree optimisation model is proposed to detect non-cooperative behaviour of experts. To ensure an optimal solution for the minimum cost feedback model, a weight update method is proposed, considering the trust relationship between experts. A detailed analysis regarding the selection of tolerance thresholds to prevent over-penalisation of weights of non-collaborators is reported. Finally, comprehensive numerical and comparative analyses are presented to validate the proposed method.
Open Access
Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set.
(Elsevier, 2013) Greenfield, Sarah; Chiclana, Francisco
The work reported in this paper addresses the challenge of the efficient and accurate defuzzification of discretised interval type-2 fuzzy sets. The exhaustive method of defuzzification for type-2 fuzzy sets is extremely slow, owing to its enormous computational complexity. Several approximate methods have been devised in response to this bottleneck. In this paper we survey four alternative strategies for defuzzifying an interval type-2 fuzzy set: 1. The Karnik-Mendel Iterative Procedure, 2. the Wu-Mendel Approximation, 3. the Greenfield-Chiclana Collapsing Defuzzifier, and 4. the Nie-Tan Method. We evaluated the different methods experimentally for accuracy, by means of a comparative study using six representative test sets with varied characteristics, using the exhaustive method as the standard. A preliminary ranking of the methods was achieved using a multi-criteria decision making methodology based on the assignment of weights according to performance. The ranking produced, in order of decreasing accuracy, is 1. the Collapsing Defuzzifier, 2. the Nie-Tan Method, 3. the Karnik-Mendel Iterative Procedure, and 4. the Wu-Mendel Approximation. Following that, a more rigorous analysis was undertaken by means of the Wilcoxon Nonparametric Test, in order to validate the preliminary test conclusions. It was found that there was no evidence of a significant difference between the accuracy of the Collapsing and Nie-Tan Methods, and between that of the Karnik-Mendel Iterative Procedure and the Wu-Mendel Approximation. However, there was evidence to suggest that the collapsing and Nie-Tan Methods are more accurate than the Karnik-Mendel Iterative Procedure and the Wu-Mendel Approximation. In relation to efficiency, each method’s computational complexity was analysed, resulting in a ranking (from least computationally complex to most computationally complex) as follows: 1. the Nie-Tan Method, 2. the Karnik-Mendel Iterative Procedure (lowest complexity possible), 3. the Greenfield-Chiclana Collapsing Defuzzifier, 4. the Karnik-Mendel Iterative Procedure (highest complexity possible), and 5. the Wu-Mendel Approximation.
Open Access
Adapting Traffic Simulation for Traffic Management: A Neural Network Approach
(2013-10) Passow, Benjamin N.; Elizondo, David; Chiclana, Francisco; Witheridge, S.; Goodyer, E. N.
Static models and simulations are commonly used in urban traffic management but none feature a dynamic element for near real-time traffic control. This work presents an artificial neural network forecaster methodology applied to traffic flow condition prediction. The spatially distributed architecture uses life-long learning with a novel adaptive Artificial Neural Network based filter to detect and remove outliers from training data. The system has been designed to support traffic engineers in their decision making to react to traffic conditions before they get out of control. We performed experiments using feed-forward backpropagation, cascade-forward back-propagation, radial basis, and generalized regression Artificial Neural Networks for this purpose. Test results on actual data collected from the city of Leicester, UK, confirm our approach to deliver suitable forecasts.
Metadata only
Additive consistency as a tool to solve group decision making problems
(2004) Chiclana, Francisco; Alonso, S.; Herrera, F.; Herrera-Viedma, Enrique
Metadata only
Additive consistency of fuzzy preference relations: characterization and construction.
(2003) Herrera, F.; Herrera-Viedma, Enrique; Chiclana, Francisco
Embargo
Addressing the influence of limited tolerance and compromise behaviors on the social trust network consensus-reaching process
(Elsevier, 2024-08-06) Zhang, Hengjie; Liu, Shenghua; Li, Cong-Cong; Dong, Yucheng; Chiclana, Francisco; Herrera-Viedma, Enrique
In social trust network group decision-making, experts typically show limited tolerance and compromise behaviors when modifying their opinions to reach consensus. The first behavior implies that an expert will change their opinion without cost if the suggested opinion closely aligns with that of trusted experts. The second behavior implies that an expert will accept the suggested opinion only if it falls within a predefined compromise boundary relative to trusted experts’ opinions. However, existing maximum expert consensus models (MECMs) do not adequately consider these behaviors, limiting their practical applicability. To address this gap, this study proposes a social trust MECM with budget constraints. Budget constraints can lead to an insufficient number of experts within the consensus, underscoring the need for higher budget allocation to achieve consensus. To address this issue, a minimum cost consensus model (MCCM) considering network-dependent limited tolerance and compromise behaviors (NDLTCBs) was developed to provide a budget increment reference. Notably, network-dependent limited compromise behavior is crucial in the MCCM, especially when compromise values are small, as it may prevent feasible solutions. In such cases, a minimum compromise increment consensus model is created to determine the necessary increase in compromise values for a feasible MCCM solution. Subsequently, an interactive maximum expert consensus-reaching process is introduced. Simulation experiments demonstrate that consensus efficiency, in terms of the number of experts within the consensus, can be enhanced by considering NDLTCBs.
Open Access
Aggregation of Unbalanced Fuzzy Linguistic Information in Group Decision Problems based on Type-1 OWA Operator.
(2015) Mata, F.; Perez, L. G.; Chiclana, Francisco; Herrera-Viedma, Enrique
Information aggregation is a key task in any group decision making problem. In the fuzzy linguistic context, when comparing two alternatives, it is usually assumed that assessments belong to linguistic term sets of symmetrically distributed labels with respect to a central label that stands for the indifference state. However, in practice there are many situations whose nature recommends their modelling using not symmetric linguistic term sets, and therefore formal approaches to deal with sets of unbalanced linguistic labels in decision making are necessary to be appropriately developed. In literature, the linguistic hierarchy methodology has proved successful when modelling unbalanced linguistic labels using an ordinal approach in their representation. However, linguistic labels can be modelled using a cardinal approach, i.e. as fuzzy subsets represented by membership functions. Obviously, the linguistic hierarchy methodology is not appropriate in these cases. In this contribution, a Type-1 OWA approach is proposed to deal with the aggregation step of the resolution process of a group decision making problem with unbalanced linguistic information modelled using a cardinal approach. The Type-1 OWA operator aggregates fuzzy sets and uses whole membership functions to compute the aggregated output fuzzy sets. The application of the Type-1 OWA approach to an example where the linguistic hierarchy approach was applied before will provide us an opportunity to compare the aggregated results obtained in both cases. Following the defuzzification of the Type-1 OWA aggregated values, it can be concluded that both methodologies are equivalent. The use of the Type-1 OWA approach in this decision making context does not require building linguistic hierarchies while at the same time allows a fully exploitation of the fuzzy nature of linguistic information.
Open Access
Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations
(IEEEXplore, 2019-07) 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.
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Alpha-level aggregation: A practical approach to type-1 OWA operation for aggregating uncertain information with applications to breast cancer treatments.
(IEEE, 2010) Zhou, Shang-Ming; Chiclana, Francisco; John, Robert, 1955-; Garibaldi, J. M.
Open Access
An alternative calculation of the consensus degree in group decision making problems
(Elsevier, 2017-12-12) del Moral, Maria Jose; Chiclana, Francisco; Tapia Garcia, Juan Miguel; Herrera-Viedma, Enrique
In a problem of group decision-making it is desirable to obtain a solution with the highest possible degree of agreement –consensus- among the participants. For this aim, it is necessary to have tools that facilitate the calculation of the degree of consensus in a reliable way. This study proposes a consensus index based on a statistical measure of variability of the preferences expressed by the experts in a group decision-making process and performs a specific comparative study between this index and several known consensus measures. The analysis shows that in this specific situation the proposed measure behaves in a similar way to the previous ones and it could play their role in a process of decision making in group.
Embargo
An endogenous and continual learning approach to personalize individual semantics to support linguistic consensus reaching
(Elsevier, 2024-08-14) Wu, Yuzhu; Li, Zhaojin; Gao, Yuan; Chiclana, Francisco; Chen, Xia; Dong, Yucheng
In linguistic group decision making, it is known that decision makers are individualized in understanding the meanings of words, i.e., decision makers have personalized individual semantics (PISs) in the representation of linguistic preferences. Since individuals influence each other mutually in the consensus reaching process, PISs will accordingly change. This suggests that there is an updating process of PISs for individuals. This paper proposes an endogenous and continual learning-based approach to update PISs in consensus reaching process by incorporating the endogenous consistency-driven PIS model and continual PIS learning based consensus model. Through this approach, individuals’ PISs are endogenously updated and learned while ensuring an optimal level of consistency and an increased level of collective consensus during consensus reaching process. At the end of the study, numerical examples and some simulation and comparative analyses are presented to justify the effectiveness of proposed approach
Embargo
An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship
(IEEE, 2024-01-24) Xing, Yumei; Wu, Jian; Chiclana, Francisco; Liang, Changyong; Yager, Ronald R.
In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship. First, the supremum and infimum incentives-based rule driven by trust relationship is defined. Based on the assumption that if incentive conditions are met, then experts will be willing to adjust their preferences, the incentive behavior-driven minimum adjustment consensus model is developed to generate optimal incentive-based recommendation preferences. Thus, the proposed incentive mechanism can effectively reduce the preference adjustment cost and promote group consensus reaching. Third, the updated trust relationships between experts are shown to be strengthen by the proposed incentive-driven preference revision. Consequently, the optimization model based on trust interaction relationship is constructed to obtain the final group preference matrix. Finally, a supplier selection case of high-end medical equipment is provided to illustrate the proposed method and show the rationality and advantages of the proposed methodology with both a sensitivity analysis and a comparison analysis.
Open Access
An integrated decision framework for evaluating and recommending health care services
(Springer, 2024-03-21) Alshouha, Bashar; Serrano-Guerrero, Jesus; Chiclana, Francisco; Romero, Francisco P.; Olivas, Jose A.
Quality management techniques such as the quality function deployment model can help hospitals assess and improve the quality of their services by integrating the voice of customers. The different quality parameters of this model are usually determined and assessed by experts; nonetheless, obtaining such experts is not always easy or inexpensive. Moreover, in this method, patient opinions are not usually considered directly, although they are the real users of the services and those who can best assess those services. Nevertheless, these opinions are easily accessible today, owing to the development of medical social networks where patients directly convey their opinions about the different services and features of a hospital. Therefore, it is feasible to replace expert knowledge with the information provided by these opinions. Based on this idea, this study proposes a novel fuzzy recommendation model based on the quality function deployment method to rank hospitals depending on patient opinions and preferences regarding hospital services. This model integrates a topic modeling strategy for determining hospital requirements, customer needs, and the relationship between them as well as a sentiment analysis algorithm for assessing customer satisfaction regarding hospital services. To demonstrate the usefulness of the proposed method, several experiments were conducted using patient reviews from real hospitals, and the method was compared against other recommendation models. The results prove that this approach represents a step toward more personalized and effective health care system selection considering patient preferences and opinions.
Open Access
An analysis of consensus approaches based on different concepts of coincidence
(IOS Press, 2018-04-19) del Moral, Maria Jose; Tapia Garcia, Juan Miguel; Chiclana, Francisco; Al-Hmouz, A; Herrera-Viedma, Enrique
Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process.

Browsing by Author "Chiclana, Francisco"

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