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Dr. Luis Martinez
University of Jaén

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0 Decision Analysis
0 Recommender Systems
0 Consensus
0 Decision Making under Uncertainty
0 computing with words

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Decision Making under Uncertainty
Consensus
Recommender Systems
computing with words
Decision Analysis

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Journal article
Published: 13 August 2021 in Expert Systems with Applications
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Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness.

ACS Style

Long-Hao Yang; Jun Liu; Ying-Ming Wang; Chris Nugent; Luis Martínez. Online updating extended belief rule-based system for sensor-based activity recognition. Expert Systems with Applications 2021, 186, 115737 .

AMA Style

Long-Hao Yang, Jun Liu, Ying-Ming Wang, Chris Nugent, Luis Martínez. Online updating extended belief rule-based system for sensor-based activity recognition. Expert Systems with Applications. 2021; 186 ():115737.

Chicago/Turabian Style

Long-Hao Yang; Jun Liu; Ying-Ming Wang; Chris Nugent; Luis Martínez. 2021. "Online updating extended belief rule-based system for sensor-based activity recognition." Expert Systems with Applications 186, no. : 115737.

Research article
Published: 26 July 2021 in International Journal of Intelligent Systems
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Consensus Reaching Processes (CRPs) deal with those group decision-making situations in which conflicts among experts' opinions make difficult the reaching of an agreed solution. This situation, worsens in large-scale group decision situations, in which opinions tend to be more polarized, because in problems with extreme opinions it is harder to reach an agreement. Several studies have shown that experts' preferences may not always follow a linear scale, as it has commonly been assumed in previous CRP. Therefore, the main aim of this paper is to study the effect of modeling this nonlinear behavior of experts' preferences (expressed by fuzzy preference relations) in CRPs. To do that, the experts' preferences will be remapped by using nonlinear deformations which amplify or reduce the distance between the extreme values. We introduce such automorphisms to remap the preferences as Extreme Values Amplifications (EVAs) and Extreme Values Reductions (EVRs), study their main properties and propose several families of these EVA and EVR functions. An analysis about the behavior of EVAs and EVRs when are implemented in a generic consensus model is then developed. Finally, an illustrative experiment to study the performance of different families of EVAs in CRPs is provided.

ACS Style

Diego García‐Zamora; Álvaro Labella; Rosa M. Rodríguez; Luis Martínez. Nonlinear preferences in group decision‐making. Extreme values amplifications and extreme values reductions. International Journal of Intelligent Systems 2021, 1 .

AMA Style

Diego García‐Zamora, Álvaro Labella, Rosa M. Rodríguez, Luis Martínez. Nonlinear preferences in group decision‐making. Extreme values amplifications and extreme values reductions. International Journal of Intelligent Systems. 2021; ():1.

Chicago/Turabian Style

Diego García‐Zamora; Álvaro Labella; Rosa M. Rodríguez; Luis Martínez. 2021. "Nonlinear preferences in group decision‐making. Extreme values amplifications and extreme values reductions." International Journal of Intelligent Systems , no. : 1.

Journal article
Published: 30 June 2021 in Expert Systems with Applications
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Group recommender systems have emerged as a solution to recommend interesting, suitable, and useful items that are consumed socially by groups of people, rather than individually. Such systems have pushed for the use of new recommendation methods within such an emerging scenario, in which the use of the collaborative filtering paradigm is the core of the recommender algorithm. However, collaborative filtering presents several drawbacks and limitations in this scenario, such as the need for lots of rating values, as well as their co-occurrence across several items and users (scarcity). In order to overcome these drawbacks, this research explores a taxonomy for content-based group recommendation systems (CB-GRS), and subsequently the paper discusses and analyzes three specific models that can be used to build CB-GRS, which are (1) CB-GRSs supported by recommendation aggregation and individual ranking, (2) CB-GRSs supported by recommendation aggregation and user-item matching, and (3) CB-GRSs supported by the aggregation of user profiles. Furthermore, the paper presents a hybrid CB-GRS that combines the models (2) and (3) and integrates feature weighting and aggregation function switching. An experimental protocol over well-known datasets is then developed in order to evaluate the proposals. The current study aims at providing a basis to develop a research branch concerning content-based group recommender systems.

ACS Style

Yilena Pérez-Almaguer; Raciel Yera; Ahmad A. Alzahrani; Luis Martínez. Content-based group recommender systems: a general taxonomy and further improvements. Expert Systems with Applications 2021, 184, 115444 .

AMA Style

Yilena Pérez-Almaguer, Raciel Yera, Ahmad A. Alzahrani, Luis Martínez. Content-based group recommender systems: a general taxonomy and further improvements. Expert Systems with Applications. 2021; 184 ():115444.

Chicago/Turabian Style

Yilena Pérez-Almaguer; Raciel Yera; Ahmad A. Alzahrani; Luis Martínez. 2021. "Content-based group recommender systems: a general taxonomy and further improvements." Expert Systems with Applications 184, no. : 115444.

Journal article
Published: 15 June 2021 in Knowledge-Based Systems
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The use of the hesitant fuzzy linguistic term sets (HFLTSs) has recently become an important trend in fuzzy decision making, and aggregating HFLTSs and their extensions has now become crucial for making decisions. Previous approaches to aggregating possibility distributions for HFLTSs were based on the paradigm of computing with words, whereas few proposals have been made to aggregate HFLTS possibility distributions under the framework of statistical data analysis so as to reduce information loss and distortion. An initial attempt was the similarity-measure-based agglomerative hierarchical clustering (SM-AggHC) two-stage aggregation paradigm for HFLTS possibility distributions, which, however, presents some important performance limitations from time complexity and memory requirement perspectives. Thereby, this paper introduces a new approach, so called, “N-two-stage algorithmic aggregation paradigm driven by the K-means clustering” (N2S-KMC) to overcome these limitations by cardinality reduction in the first stage of the aggregation process. The subsequent stage uses the similarity-measure-based K-means clustering algorithm to outperform the SM-AggHC algorithm. Such an outperformance, from run time and memory usage, is demonstrated by experimental results.

ACS Style

Zhen-Song Chen; Xuan Zhang; Witold Pedrycz; Xian-Jia Wang; Kwai-Sang Chin; Luis Martínez. K-means clustering for the aggregation of HFLTS possibility distributions: N-two-stage algorithmic paradigm. Knowledge-Based Systems 2021, 227, 107230 .

AMA Style

Zhen-Song Chen, Xuan Zhang, Witold Pedrycz, Xian-Jia Wang, Kwai-Sang Chin, Luis Martínez. K-means clustering for the aggregation of HFLTS possibility distributions: N-two-stage algorithmic paradigm. Knowledge-Based Systems. 2021; 227 ():107230.

Chicago/Turabian Style

Zhen-Song Chen; Xuan Zhang; Witold Pedrycz; Xian-Jia Wang; Kwai-Sang Chin; Luis Martínez. 2021. "K-means clustering for the aggregation of HFLTS possibility distributions: N-two-stage algorithmic paradigm." Knowledge-Based Systems 227, no. : 107230.

Review article
Published: 11 June 2021 in Expert Systems with Applications
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Multi-Criteria Decision Making (MCDM) is a complex process. It aims to support decision makers in making their decisions more effective and consistent. MCDM provides a useful and successful alternative for handling three main types of MCDM problems, namely, choosing, ranking and sorting. The first two are the most common problems studied but the third offers a way to deal with real world MCDM problems that require alternatives to be assigned to ordered categories. The practitioners are currently developing and applying sorting methods to solve problems from different application areas. In spite of its interest and applicability, there is only one previous review on Multiple-Criteria sorting, performed almost 20 years ago. Hence, because of its interest this paper presents a new and systematic review of MCDM sorting methods that includes 30 years of research in the field. This review has systematically analyzed the conventional and non-classical methods of MCDM sorting and then classified the papers published into 16 application areas. The analysis reveals that the methodological development is still in growth phase for MCDM sorting and discovers the applied methods’ trends. It also shows the complete spectrum of the areas of the application addressed, the state of knowledge about methods, the type of contribution to the knowledge, and the application area for the four categories of the MCDM approaches. The systematic review scrutinizes each selected article in order to find out which approach from the multi-criteria sorting presents the most development based on its contribution and application of the methods. We also aim to discover which Multiple-Criteria sorting methods are the most studied in MCDM. The relevant finding is the relation between the most applied methods and the application areas together with future directions for further research.

ACS Style

Pavel Anselmo Alvarez; Alessio Ishizaka; Luis Martínez. Multiple-criteria decision-making sorting methods: A survey. Expert Systems with Applications 2021, 183, 115368 .

AMA Style

Pavel Anselmo Alvarez, Alessio Ishizaka, Luis Martínez. Multiple-criteria decision-making sorting methods: A survey. Expert Systems with Applications. 2021; 183 ():115368.

Chicago/Turabian Style

Pavel Anselmo Alvarez; Alessio Ishizaka; Luis Martínez. 2021. "Multiple-criteria decision-making sorting methods: A survey." Expert Systems with Applications 183, no. : 115368.

Journal article
Published: 11 May 2021 in IEEE Transactions on Fuzzy Systems
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Linguistic multiple attribute decision making (LMADM) has been widely used in different decision contexts to achieve a final solution, e.g., obtaining the ranking of alternatives. Evaluation information using linguistic scales in LMADM is usually expressed in simple discrete linguistic terms. In this paper, we consider that each simple linguistic term in LMADM actually has a continuous representation (denoted as a linguistic 2-tuple) whose rounding operation matches the linguistic term. Under such conditions, the ranking of alternatives based on simple linguistic terms may not be consistent with that based on linguistic 2-tuples. Thus, this paper studies the linguistic scale ranking consistency issue in LMADM under seven classical and commonly used decision rules: weighted averaging, ordered weighted averaging, weighted geometric averaging, ordered weighted geometric averaging, PROMETHEE, TOPSIS, and ELECTRE. We first define the concept of the linguistic scale ranking consistency in LMADM. Afterwards, several consistency conditions are presented analytically for the selected decision rules to guarantee the linguistic scale ranking consistency. Finally, we present detailed theoretical and simulation-based comparisons. The theoretical comparisons show the roles of decision rules and granularity in the consistency conditions, and the simulation-based comparisons demonstrate the performance of the selected decision rules in defending against the ranking inconsistency in LMADM.

ACS Style

Sihai Zhao; Yucheng Dong; Luis Martinez; Witold Pedrycz. Analysis of Ranking Consistency in Linguistic Multiple Attribute Decision Making: The Roles of Granularity and Decision Rules. IEEE Transactions on Fuzzy Systems 2021, PP, 1 -1.

AMA Style

Sihai Zhao, Yucheng Dong, Luis Martinez, Witold Pedrycz. Analysis of Ranking Consistency in Linguistic Multiple Attribute Decision Making: The Roles of Granularity and Decision Rules. IEEE Transactions on Fuzzy Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Sihai Zhao; Yucheng Dong; Luis Martinez; Witold Pedrycz. 2021. "Analysis of Ranking Consistency in Linguistic Multiple Attribute Decision Making: The Roles of Granularity and Decision Rules." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Article
Published: 05 May 2021 in Group Decision and Negotiation
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Large-scale group decision-making (LSGDM) deals with complex decision- making problems which involve a large number of decision makers (DMs). Such a complex scenario leads to uncertain contexts in which DMs elicit their knowledge using linguistic information that can be modelled using different representations. However, current processes for solving LSGDM problems commonly neglect a key concept in many real-world decision-making problems, such as DMs’ regret aversion psychological behavior. Therefore, this paper introduces a novel consensus based linguistic distribution LSGDM (CLDLSGDM) approach based on a statistical inference principle that considers DMs’ regret aversion psychological characteristics using regret theory and which aims at obtaining agreed solutions. Specifically, the CLDLSGDM approach applies the statistical inference principle to the consensual information obtained in the consensus process, in order to derive the weights of DMs and attributes using the consensus matrix and adjusted decision-making matrices to solve the decision-making problem. Afterwards, by using regret theory, the comprehensive perceived utility values of alternatives are derived and their ranking determined. Finally, a performance evaluation of public hospitals in China is given as an example in order to illustrate the implementation of the designed method. The stability and advantages of the designed method are analyzed by a sensitivity and a comparative analysis.

ACS Style

Feifei Jin; Jinpei Liu; Ligang Zhou; Luis Martínez. Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory. Group Decision and Negotiation 2021, 1 -33.

AMA Style

Feifei Jin, Jinpei Liu, Ligang Zhou, Luis Martínez. Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory. Group Decision and Negotiation. 2021; ():1-33.

Chicago/Turabian Style

Feifei Jin; Jinpei Liu; Ligang Zhou; Luis Martínez. 2021. "Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory." Group Decision and Negotiation , no. : 1-33.

Journal article
Published: 04 May 2021 in Technological and Economic Development of Economy
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We are currently witnessing the development of a set of organizations that have been entrusted with meeting the very diverse needs of citizens. As a result, they receive funds, in order to ensure they are managed appropriately. The transparency of the information revealed by Non-profit Organizations (NPOs) has become of increasing interest to public authorities and research. However, very few studies empirically measure the extent of transparency in NPOs. Only a handful checked the compliance of various indicators, lacking agreement on which ones to include and their weighting. To address this issue, this study empirically validates the weighting of the indicators from the alliance between the Platform for Social Action NGO and the Spanish Coordinator for Development NGO (CONGDE) document with experts in NPOs’ opinions. We use the Best-Worst Method (BWM) to optimally assign weights to multi-criteria decision making situations. Our results show interesting differences in the level of importance given to the indicators by public authorities and experts, suggesting the need for a revision of the importance proposed.

ACS Style

Antonio Luis Moreno-Albarracín; Cristina Ortega-Rodríguez; Ana Licerán-Gutiérrez; Álvaro Labella; Luis Martínez. TRANSPARENCY INDICATORS TO IMPROVE ACCOUNTABILITY FOR NON-PROFIT ORGANIZATIONS: A SPANISH CASE STUDY. Technological and Economic Development of Economy 2021, 27, 763 -782.

AMA Style

Antonio Luis Moreno-Albarracín, Cristina Ortega-Rodríguez, Ana Licerán-Gutiérrez, Álvaro Labella, Luis Martínez. TRANSPARENCY INDICATORS TO IMPROVE ACCOUNTABILITY FOR NON-PROFIT ORGANIZATIONS: A SPANISH CASE STUDY. Technological and Economic Development of Economy. 2021; 27 (3):763-782.

Chicago/Turabian Style

Antonio Luis Moreno-Albarracín; Cristina Ortega-Rodríguez; Ana Licerán-Gutiérrez; Álvaro Labella; Luis Martínez. 2021. "TRANSPARENCY INDICATORS TO IMPROVE ACCOUNTABILITY FOR NON-PROFIT ORGANIZATIONS: A SPANISH CASE STUDY." Technological and Economic Development of Economy 27, no. 3: 763-782.

Journal article
Published: 08 April 2021 in Information Sciences
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Interval reciprocal preference relations (IRPRs) have been extensively applied to real-life decision-making problems. This paper aims at introducing two new decision-making methods with IRPRs.To overcome drawbacks of several extant order relations of intervals, a new admissible order relation of intervals is proposed. An algorithm is then introduced to rank a series of intervals. We define a new consistency index and the satisfactory consistency of IRPRs. For improving the consistency level of IRPRs, a linear programming model is built. Subsequently, a new individual decision-making (IDM) method with an IRPR is introduced. For group decision making, a group consensus index is proposed. To improve the group consensus degree, an interactive convergent iterative algorithm is designed. Decision makers’ weights are determined by combining the logarithmic Manhattan distance between two IRPRs with the consistency indices of IRPRs. Accordingly, a novel consistent and consensus-based group decision-making (GDM) method with IRPRs is presented. Eventually, a decision support system is developed based on the proposed IDM method and GDM method. Illustrative examples and simulation experiments are provided to illustrate the superiority of the proposed IDM method and GDM method.

ACS Style

Xian-Juan Cheng; Shu-Ping Wan; Jiu-Ying Dong; Luis Martínez. New decision-making methods with interval reciprocal preference relations: A new admissible order relation of intervals. Information Sciences 2021, 569, 400 -429.

AMA Style

Xian-Juan Cheng, Shu-Ping Wan, Jiu-Ying Dong, Luis Martínez. New decision-making methods with interval reciprocal preference relations: A new admissible order relation of intervals. Information Sciences. 2021; 569 ():400-429.

Chicago/Turabian Style

Xian-Juan Cheng; Shu-Ping Wan; Jiu-Ying Dong; Luis Martínez. 2021. "New decision-making methods with interval reciprocal preference relations: A new admissible order relation of intervals." Information Sciences 569, no. : 400-429.

Research article
Published: 08 March 2021 in International Journal of Intelligent Systems
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This study develops a power‐average‐operator‐based hybrid multiattribute online product recommendation model that considers the consumer's risk attitude to rank categoric product options as a complement to existing recommender systems. Online production recommendation plays a key role in the development of e‐commerce, and can greatly improve consumers' shopping experiences. However, few online shopping sites provide interactive decision aids for consumers such that they can articulate their preferences towards multiple selection attributes with the purpose of mitigating choice difficulty and improving decision quality. Additionally, consumers' risk attitudes to online shopping dramatically impact their product choices. In the model proposed in this paper, the risk attitude‐based power average (RAPA) operator is used to integrate the risk attitude of the decision‐maker into the information fusion process of multiple attribute decision‐making. Subsequently, the risk attitude function, with several basic types, is introduced to quantify the risk attitude of the decision‐maker for use in the RAPA operator. A proportional hesitant fuzzy 2‐tuple linguistic term set (PHF2TLTS) is constructed by incorporating a binary of linguistic information aiming to comprehensively analyze the hybrid product information. With a focus on the information fusion process, the proportional hesitant 2‐tuple linguistic RAPA operator and weighted proportional hesitant 2‐tuple linguistic RAPA operator are introduced to aggregate a given set of PHF2TLTSs. The validity of the proposed model is demonstrated using an illustrative example, a comparison with existing approaches and detailed explanations of the performance differences.

ACS Style

Zhen‐Song Chen; Lan‐Lan Yang; Rosa M. Rodríguez; Sheng‐Hua Xiong; Kwai‐Sang Chin; Luis Martínez. Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making. International Journal of Intelligent Systems 2021, 36, 2572 -2617.

AMA Style

Zhen‐Song Chen, Lan‐Lan Yang, Rosa M. Rodríguez, Sheng‐Hua Xiong, Kwai‐Sang Chin, Luis Martínez. Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making. International Journal of Intelligent Systems. 2021; 36 (6):2572-2617.

Chicago/Turabian Style

Zhen‐Song Chen; Lan‐Lan Yang; Rosa M. Rodríguez; Sheng‐Hua Xiong; Kwai‐Sang Chin; Luis Martínez. 2021. "Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making." International Journal of Intelligent Systems 36, no. 6: 2572-2617.

Journal article
Published: 22 February 2021 in Sustainability
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Despite the substantial efforts of governments in promoting sustainable development, there exists a considerable debate regarding the environmental policy making approach under information ambiguity and competition. This study investigates market competition between a green and a non-green supply chain (SC) under two government regulation policies, namely, selling price and production quantities. To tackle the policy making challenges, a fuzzy game theoretical model was employed in a centralized and decentralized SC setting. The results revealed that SCs always achieve a higher expected profit under a decentralized structure, regardless of the type of the governments intervention policy. Also, the government’s policy making success was found to be highly dependent on the channel leadership, market competition, and the SC structure. Our findings suggest that the policy makers’ objectives in reducing environmental pollution and increasing revenue are highly achievable, without risk of losing channel coordination and maximum level of efficiency.

ACS Style

Mina Rahimi; Ashkan Hafezalkotob; Sobhan Asian; Luis Martínez. Environmental Policy making in Supply Chains under Ambiguity and Competition: A Fuzzy Stackelberg Game Approach. Sustainability 2021, 13, 2367 .

AMA Style

Mina Rahimi, Ashkan Hafezalkotob, Sobhan Asian, Luis Martínez. Environmental Policy making in Supply Chains under Ambiguity and Competition: A Fuzzy Stackelberg Game Approach. Sustainability. 2021; 13 (4):2367.

Chicago/Turabian Style

Mina Rahimi; Ashkan Hafezalkotob; Sobhan Asian; Luis Martínez. 2021. "Environmental Policy making in Supply Chains under Ambiguity and Competition: A Fuzzy Stackelberg Game Approach." Sustainability 13, no. 4: 2367.

Journal article
Published: 18 February 2021 in Automation in Construction
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The process of bid evaluation is subject to indetermination, imprecision, and uncertainty in terms of their alternative-criterion decision appraisals. To address this issue, this paper develops a novel ELECTRE III-based MCGDM approach for bid evaluation, in which generalized comparative linguistic expressions (GCLEs) are used to evaluate bidder performance. The collected GCLEs are further transformed into possibility-distribution-based hesitant fuzzy linguistic-term sets (HFLTSs) to facilitate the qualitative construction of individual expertise. A consensus-reaching process is introduced to promote decisions that are agreed upon by experts and thereby ensure an enhanced state of mutual agreement among the experts. Subsequently, an integrated subjective-objective approach is proposed to calculate criterion weights and to implement an ELECTRE III-based method that incorporates HFLTS possibility distributions, which allows us to treat the indetermination, imprecision, and uncertainty embedded in appraisals of alternative-criterion decisions when evaluating bids. The potential advantages of the proposed approach are validated by a real-life contractor-selection case.

ACS Style

Zhen-Song Chen; Xuan Zhang; Rosa M. Rodríguez; Witold Pedrycz; Luis Martínez. Expertise-based bid evaluation for construction-contractor selection with generalized comparative linguistic ELECTRE III. Automation in Construction 2021, 125, 103578 .

AMA Style

Zhen-Song Chen, Xuan Zhang, Rosa M. Rodríguez, Witold Pedrycz, Luis Martínez. Expertise-based bid evaluation for construction-contractor selection with generalized comparative linguistic ELECTRE III. Automation in Construction. 2021; 125 ():103578.

Chicago/Turabian Style

Zhen-Song Chen; Xuan Zhang; Rosa M. Rodríguez; Witold Pedrycz; Luis Martínez. 2021. "Expertise-based bid evaluation for construction-contractor selection with generalized comparative linguistic ELECTRE III." Automation in Construction 125, no. : 103578.

Journal article
Published: 11 February 2021 in Applied Soft Computing
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Unlike other linguistic modelings, probabilistic linguistic terms can clearly describe the importance of different linguistic terms. With respect to group decision-making (GDM) problems, it is convenient for experts to express their evaluation opinions with probabilistic linguistic preference relations (PLPRs), which can transform experts’ quantitative descriptions into qualitative probabilistic linguistic terms. The processes of consistency-adjustment and expert weights determination play a key role in GDM. Therefore, this paper aims at the design of a novel probabilistic linguistic GDM method with consistency-adjustment algorithm and trust relationship-driven expert weight determination model. First, we redefine the multiplicative consistency of PLPRs, which only involves changing the probabilities of linguistic terms. A new distance between PLPRs is presented to calculate the consistency index. Then, we propose a convergent consistency-adjustment algorithm to improve the consistency of a PLPR to an acceptable consistency level. Subsequently, a trust relationship-driven expert weight determination model is developed to derive the experts’ weights in a social network environment. Finally, a probabilistic linguistic GDM method is designed to determine the reliable ranking of alternatives. The advantages and applicability of the proposed method are illustrated by a case study concerning an evaluation of logistics service suppliers and associated comparative analyses.

ACS Style

Feifei Jin; Meng Cao; Jinpei Liu; Luis Martínez; Huayou Chen. Consistency and trust relationship-driven social network group decision-making method with probabilistic linguistic information. Applied Soft Computing 2021, 103, 107170 .

AMA Style

Feifei Jin, Meng Cao, Jinpei Liu, Luis Martínez, Huayou Chen. Consistency and trust relationship-driven social network group decision-making method with probabilistic linguistic information. Applied Soft Computing. 2021; 103 ():107170.

Chicago/Turabian Style

Feifei Jin; Meng Cao; Jinpei Liu; Luis Martínez; Huayou Chen. 2021. "Consistency and trust relationship-driven social network group decision-making method with probabilistic linguistic information." Applied Soft Computing 103, no. : 107170.

Journal article
Published: 10 February 2021 in IEEE Transactions on Fuzzy Systems
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Ranking of Fuzzy Numbers (FNs) is a key stage within Fuzzy Multi-Criteria Decision Analysis (FMCDA). However, the influence of FNs dependence on their ranking, including ranking alternatives within FMCDA, has not been studied yet. In this paper, for studying such an influence, the widely-used defuzzification based fuzzy ranking methods, Centroid Index and Integral of Means, along with their modifications, which are pairwise comparison defuzzification ranking methods, are explored. The authors argue that classical defuzzification ranking methods are intended to deal with independent FNs, whereas their modifications may be used for ranking of dependent FNs.It is proved, pairwise comparison Yuan's and defuzzification Integral of Means ranking methods are equivalent when ordering independent and can differ when ordering dependent FNs; at the same time, Yuan's and modified Integral of Means ranking methods are equivalent when ordering both independent and dependent FNs. Intransitivity of the two modified ranking methods when ordering dependent FNs as well as intransitivity of alternatives in FMCDA for Fuzzy Multi-Attribute Value Theory (FMAVT) as an example is proved. The distinctions in ranking of dependent FNs by all ranking methods under consideration are explored through ordering alternatives within FMAVT. For this, a real life case study is considered, and the distinctions in ordering alternatives by classical and modified ranking methods are demonstrated. Statistical analysis of distinctions in ordering alternatives by FMAVT with different ranking methods is implemented with the use of Monte Carlo simulation. The significance of distinctions for the choice and ranking multi-criteria problems as well as for justification of utilizing ranking methods under consideration in FMCDA are discussed.

ACS Style

Boris Yatsalo; Alexander Korobov; Alexander Radaev; Jindong Qin; Luis Martinez. Ranking of Independent and Dependent Fuzzy Numbers and Intransitivity in Fuzzy MCDA. IEEE Transactions on Fuzzy Systems 2021, PP, 1 -1.

AMA Style

Boris Yatsalo, Alexander Korobov, Alexander Radaev, Jindong Qin, Luis Martinez. Ranking of Independent and Dependent Fuzzy Numbers and Intransitivity in Fuzzy MCDA. IEEE Transactions on Fuzzy Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Boris Yatsalo; Alexander Korobov; Alexander Radaev; Jindong Qin; Luis Martinez. 2021. "Ranking of Independent and Dependent Fuzzy Numbers and Intransitivity in Fuzzy MCDA." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Article
Published: 05 February 2021 in International Journal of Fuzzy Systems
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Membership function estimation is one of the less explored, albeit important, areas in fuzzy sets. This paper aims to define a new family of fuzzy sets called general continuous linguistic variables (GCLV), which represents a linguistic variable rather than a set of linguistic values. We refer to it as the principle of representation of linguistic variables. They are based on the well-known sigmoidal functions and contain at least three different classes of membership functions, namely, an increasing sigmoidal function, a decreasing sigmoidal function, and a convex one. These diverse features are essential to represent linguistic values exhibiting different semantics. We explore the properties of GCLV, including those ones over that allow us to approximate every continuous membership function. Finally, we illustrate the applicability of GCLV as a fuzzy tool. This leads to the development of the foundations of a new vehicle in fuzzy sets useful in data mining and time series prediction.

ACS Style

Erick González-Caballero; Rafael A. Espín-Andrade; Witold Pedrycz; Luis Martínez; Liliana A. Guerrero-Ramos. Continuous Linguistic Variables and Their Applications to Data Mining and Time Series Prediction. International Journal of Fuzzy Systems 2021, 23, 1431 -1452.

AMA Style

Erick González-Caballero, Rafael A. Espín-Andrade, Witold Pedrycz, Luis Martínez, Liliana A. Guerrero-Ramos. Continuous Linguistic Variables and Their Applications to Data Mining and Time Series Prediction. International Journal of Fuzzy Systems. 2021; 23 (5):1431-1452.

Chicago/Turabian Style

Erick González-Caballero; Rafael A. Espín-Andrade; Witold Pedrycz; Luis Martínez; Liliana A. Guerrero-Ramos. 2021. "Continuous Linguistic Variables and Their Applications to Data Mining and Time Series Prediction." International Journal of Fuzzy Systems 23, no. 5: 1431-1452.

Journal article
Published: 02 February 2021 in Computers & Industrial Engineering
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Large-scale group decision-making (LSGDM) under uncertainty modelled by comparative linguistic expressions based on a hesitant fuzzy linguistic term set (HFLTS) has recently attracted the interest of many researchers and research, due to the necessity of its function in LSGDM, and the challenges it faces such as the managing of the scalability problem, uncertainty of experts’ opinions and dealing with polarized conflicting opinions. To smooth out such discrepancies and obtain agreed solutions Consensus Reaching Processes (CRPs) for LSGDM have been applied, in which experts are grouped into sub-groups according to the closeness of their opinions to deal with scalability. However, most CRPs for LSGDM are driven by a majority rule, in which larger sub-groups, where there might be internal disagreements, lead the consensus. In such processes, the internal disagreements can produce unsatisfactory solutions. Consequently, the majority view should be complemented by additional mechanisms that also measure the strength of the sub-groups’ opinions. A good measurement of such strength is the cohesion among the sub-group members. Therefore, in this paper, a new cohesion measure for HFLTS based on restricted equivalence functions for measuring the experts’ sub-group cohesiveness is introduced to drive the consensus process together the majority and thus reduce the impact of internal disagreements risen in majority driven CRPs. It is then integrated in a new cohesion-driven CRP approach based on LSGDM to deal with comparative linguistic expressions based on HFLTS. An experimental analysis on different large scale scenarios will show the performance and importance of cohesion in consensus based LSGDM.

ACS Style

Rosa M. Rodríguez; Álvaro Labella; Mikel Sesma-Sara; Humberto Bustince; Luis Martínez. A cohesion-driven consensus reaching process for large scale group decision making under a hesitant fuzzy linguistic term sets environment. Computers & Industrial Engineering 2021, 155, 107158 .

AMA Style

Rosa M. Rodríguez, Álvaro Labella, Mikel Sesma-Sara, Humberto Bustince, Luis Martínez. A cohesion-driven consensus reaching process for large scale group decision making under a hesitant fuzzy linguistic term sets environment. Computers & Industrial Engineering. 2021; 155 ():107158.

Chicago/Turabian Style

Rosa M. Rodríguez; Álvaro Labella; Mikel Sesma-Sara; Humberto Bustince; Luis Martínez. 2021. "A cohesion-driven consensus reaching process for large scale group decision making under a hesitant fuzzy linguistic term sets environment." Computers & Industrial Engineering 155, no. : 107158.

Journal article
Published: 02 February 2021 in Computers & Industrial Engineering
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Multi-criteria group decision making (MCGDM) deals with decision makers who evaluate alternatives over several criteria. MCGDM problems evolve in tandem with the progress of our society. Such progress has given rise to the large-scale group decision making (LS-GDM) problems in which hundreds of decision makers may participate in the decision process and new challenges to face such as groups’ formation and polarization opinions. Most real world MCGDM problems present changing contexts with uncertainty that cannot be modeled by numerical values. Under these circumstances, the use of linguistic variables and computing with words (CW) processes have provided successfully results. Concretely, the 2-tuple linguistic computational model stands out because its precise linguistic computations and high interpretability. On the other hand, pairwise comparison is a widely used elicitation technique in MCGDM, but a large number of comparisons might lead inconsistent decision makers’ preferences. The Best-Worst method (BWM) reduces the number of pairwise comparisons and the inconsistency in decision makers’ opinions. Several BWM approaches have been proposed to manage linguistic information but none of them take advantage of the 2-tuple linguistic computational process based on the CW approach, which would allow to obtain precise and understandable results. This paper aims to present an extended 2-tuple BWM to reduce the number of pairwise comparisons in MCGDM problems and model the uncertainty associated with them to accomplish accuracy computations and obtaining interpretable results. Moreover, we apply our proposal to LS-GDM scenarios in which polarization opinions and sub-groups identification, ignored from any of BWM proposals, are considered. Finally, the new model is applied to several illustrative MCGDM problems.

ACS Style

Álvaro Labella; Bapi Dutta; Luis Martínez. An optimal Best-Worst prioritization method under a 2-tuple linguistic environment in decision making. Computers & Industrial Engineering 2021, 155, 107141 .

AMA Style

Álvaro Labella, Bapi Dutta, Luis Martínez. An optimal Best-Worst prioritization method under a 2-tuple linguistic environment in decision making. Computers & Industrial Engineering. 2021; 155 ():107141.

Chicago/Turabian Style

Álvaro Labella; Bapi Dutta; Luis Martínez. 2021. "An optimal Best-Worst prioritization method under a 2-tuple linguistic environment in decision making." Computers & Industrial Engineering 155, no. : 107141.

Journal article
Published: 16 January 2021 in Knowledge-Based Systems
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Nowadays, society demands group decision making (GDM) problems that require the participation of a large number of experts, so-called large scale group decision making (LS-GDM) problems. Logically, the more experts are involved in the decision making process, the more common is the emergence of disagreements in the group. For this reason, consensus reaching processes (CRPs) are key in the resolution of these problems in order to smooth such disagreements in the group and reach consensual solutions. A CRP requires that experts are receptive to change their initial preferences, but demanding excessive changes could lead to deadlocks. The well-known minimum cost consensus (MCC) model allows to obtain an agreed solution by preserving experts’ preferences as much as possible. However, this MCC model only considers the distance among experts and collective opinion, which is not enough to guarantee a desired degree of consensus. To overcome this limitation, it was proposed comprehensive MCC models (CMCC) in which both consensus degree and distance are considered, and CMCC models deal with fuzzy preference relations (FPRs) for modeling experts’ opinions. However, these models are not efficient to deal with LS-GDM problems and the FPRs consistency is ignored in them. Therefore, this paper aims to propose new CMCC models focused on LS-GDM problems in which experts use FPRs whose consistency is taken into account in order to obtain reliable results. A case study is introduced to show the effectiveness of the proposed models.

ACS Style

Rosa M. Rodríguez; Álvaro Labella; Bapi Dutta; Luis Martínez. Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations. Knowledge-Based Systems 2021, 215, 106780 .

AMA Style

Rosa M. Rodríguez, Álvaro Labella, Bapi Dutta, Luis Martínez. Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations. Knowledge-Based Systems. 2021; 215 ():106780.

Chicago/Turabian Style

Rosa M. Rodríguez; Álvaro Labella; Bapi Dutta; Luis Martínez. 2021. "Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations." Knowledge-Based Systems 215, no. : 106780.

Journal article
Published: 24 December 2020 in Applied Soft Computing
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The symbolic model based on the linguistic scale has been widely used to represent linguistic knowledge to deal with various linguistic decision problems. However, linguistic scales with different granularity may yield inconsistent decision outcomes in the linguistic decision making. Thus, this paper systematically studies the linguistic scale consistency issues in multi-granularity decision making contexts. We first define the concepts of the consistent multi-granularity representation, consistent multi-granularity aggregation and consistent multi-granularity ranking. After that, we analytically present a necessary and sufficient condition to guarantee the consistent multi-granularity representation and a sufficient condition to characterize the intrinsic mechanism of the consistent multi-granularity aggregation. Then, an attitude-based linguistic representation method (ALRM) is proposed to improve the consistent multi-granularity ranking. Finally, a detailed numerical analysis and simulation experiments are presented to show the advantages of the ALRM over the traditional linguistic approach. These results will provide new insights into the use of linguistic scales in the linguistic decision making.

ACS Style

Sihai Zhao; Yucheng Dong; Siqi Wu; Luis Martínez. Linguistic scale consistency issues in multi-granularity decision making contexts. Applied Soft Computing 2020, 101, 107035 .

AMA Style

Sihai Zhao, Yucheng Dong, Siqi Wu, Luis Martínez. Linguistic scale consistency issues in multi-granularity decision making contexts. Applied Soft Computing. 2020; 101 ():107035.

Chicago/Turabian Style

Sihai Zhao; Yucheng Dong; Siqi Wu; Luis Martínez. 2020. "Linguistic scale consistency issues in multi-granularity decision making contexts." Applied Soft Computing 101, no. : 107035.

Journal article
Published: 23 December 2020 in Mathematics
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Nowadays, decision making problems have increased their complexity and a single decision maker cannot handle these problems, with a more diverse and comprehensive view of them being necessary, which results in group decision making (GDM) schemes. The complexity of GDM problems is often due to their inherent uncertainty that is not solved just by using a group. Consequently, different methodologies has been proposed to handle it, in which, the use of the fuzzy linguistic approach stands out. Among the multiple fuzzy linguistic modeling approaches, Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) information has been recently introduced, which enhances classical linguistic modeling that is based on single terms by providing linguistic expressions in a continuous linguistic domain. Its application to decision making is quite promising, but it is necessary to develop enough operators to accomplish aggregation processes in the decision solving scheme. So far, just a small number of aggregation operators have been defined for ELICIT information. Hence, this paper aims at providing new aggregation operators for ELICIT information by developing novel OWA based operators, such as the Induced OWA (IOWA) operator in order to avoid the OWA operator needs of reordering its arguments, because ELICIT information does not have an inherent order due to its fuzzy representation. Our proposal not only consists of extending the definition of an IOWA operator for ELICIT information with crisp weights, but it is also proposed a type-1 IOWA operator for ELICIT information in which both weights and arguments are fuzzy as well as the use of ELICIT information constructing the order inducing variable to reorder the arguments. Additionally, the use of ELICIT information in GDM demands the ability to manage majority based decisions that are better represented in the IOWA operator by linguistic quantifiers. Hence, a majority-driven GDM process for ELICIT information is proposed, which it is the first proposal for fulfilling the majority solving process for GDM while using ELICIT information. Eventually, an illustrative example and a brief comparative analysis are presented in order to show the performance of the proposal and its feasibility.

ACS Style

Wen He; Bapi Dutta; Rosa M. Rodríguez; Ahmad A. Alzahrani; Luis Martínez. Induced OWA Operator for Group Decision Making Dealing with Extended Comparative Linguistic Expressions with Symbolic Translation. Mathematics 2020, 9, 20 .

AMA Style

Wen He, Bapi Dutta, Rosa M. Rodríguez, Ahmad A. Alzahrani, Luis Martínez. Induced OWA Operator for Group Decision Making Dealing with Extended Comparative Linguistic Expressions with Symbolic Translation. Mathematics. 2020; 9 (1):20.

Chicago/Turabian Style

Wen He; Bapi Dutta; Rosa M. Rodríguez; Ahmad A. Alzahrani; Luis Martínez. 2020. "Induced OWA Operator for Group Decision Making Dealing with Extended Comparative Linguistic Expressions with Symbolic Translation." Mathematics 9, no. 1: 20.