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Yucheng Dong
Business School, Sichuan University, Chengdu, China

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Journal article
Published: 03 July 2021 in Transportation Research Part E: Logistics and Transportation Review
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Stochastic shortest path (SSP) computations are often performed under very strict time constraints, so computational efficiency is critical. A major determinant for the CPU time is the number of scenarios used. We demonstrate that by carefully picking the right scenario generation method for finding scenarios, the quality of the computations can be improved substantially over random sampling for a given number of scenarios. We study extensive SSP instances from a freeway network and an urban road network, which involve 10,512 and 37,500 spatially and temporally correlated speed variables, respectively. On the basis of experimental results from a total of 42 origin–destination pairs and 6 typical objective functions for SSP problems, we find that (1) the scenario generation method generates unbiased scenarios and strongly outperforms random sampling in terms of stability (i.e., relative difference and variance) whichever origin–destination pair and objective function is used; (2) to achieve a certain accuracy, the number of scenarios required for scenario generation is much lower than that for random sampling, typically about 6–10 times lower for a stability level of 1% in the freeway network; and (3) different origin–destination pairs and different objective functions could require different numbers of scenarios to achieve a specified stability.

ACS Style

Dongqing Zhang; Stein W. Wallace; Zhaoxia Guo; Yucheng Dong; Michal Kaut. On scenario construction for stochastic shortest path problems in real road networks. Transportation Research Part E: Logistics and Transportation Review 2021, 152, 102410 .

AMA Style

Dongqing Zhang, Stein W. Wallace, Zhaoxia Guo, Yucheng Dong, Michal Kaut. On scenario construction for stochastic shortest path problems in real road networks. Transportation Research Part E: Logistics and Transportation Review. 2021; 152 ():102410.

Chicago/Turabian Style

Dongqing Zhang; Stein W. Wallace; Zhaoxia Guo; Yucheng Dong; Michal Kaut. 2021. "On scenario construction for stochastic shortest path problems in real road networks." Transportation Research Part E: Logistics and Transportation Review 152, no. : 102410.

Journal article
Published: 30 June 2021 in IEEE Transactions on Cybernetics
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Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided.

ACS Style

Cong-Cong Li; Haiming Liang; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making. IEEE Transactions on Cybernetics 2021, PP, 1 -12.

AMA Style

Cong-Cong Li, Haiming Liang, Yucheng Dong, Francisco Chiclana, Enrique Herrera-Viedma. Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making. IEEE Transactions on Cybernetics. 2021; PP (99):1-12.

Chicago/Turabian Style

Cong-Cong Li; Haiming Liang; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making." IEEE Transactions on Cybernetics PP, no. 99: 1-12.

Journal article
Published: 16 June 2021 in IEEE Transactions on Cybernetics
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In linguistic decision-making problems, there may be cases when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, that is, decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this article, we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs-based incomplete linguistic preference estimation method.

ACS Style

Cong-Cong Li; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics. IEEE Transactions on Cybernetics 2021, PP, 1 -11.

AMA Style

Cong-Cong Li, Yucheng Dong, Francisco Chiclana, Enrique Herrera-Viedma. Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics. IEEE Transactions on Cybernetics. 2021; PP (99):1-11.

Chicago/Turabian Style

Cong-Cong Li; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "Consistency-Driven Methodology to Manage Incomplete Linguistic Preference Relation: A Perspective Based on Personalized Individual Semantics." IEEE Transactions on Cybernetics PP, no. 99: 1-11.

Journal article
Published: 11 May 2021 in Computers & Industrial Engineering
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This study presents a novel consensus reaching method for multi-attribute group decision making (MAGDM) problems with preference-approval structures in prospect theory. In this method, with the consideration of the reference dependence and loss aversion behaviors of individuals, the individual prospect values of different alternatives are calculated. Then, we develop two-stage consensus reaching models with minimum adjustments to improve the consensus level among the individuals. In stage I, we assist the individuals to adjust their individual positional orderings and reference points. Next, the adjusted individual positional orderings will be as the input of the model of Stage II which aims to provide the support for individuals adjust their decision evaluations. Furthermore, a numerical example is to illustrate the feasibility of the proposal. Finally, some experiments are designed to justify the effectiveness of the proposed method. Compared with the existing studies, the proposed method can decrease the adjustments of individuals and promote the efficient of consensus reaching. The proposed method can be used to deal with some practical group decision making (e.g., the engineering bid and the urban ecological park location), where the individuals will have no conflicts of interests.

ACS Style

Jie Long; Haiming Liang; Lei Gao; Zhaoxia Guo; Yucheng Dong. Consensus reaching with two-stage minimum adjustments in multi-attribute group decision making: A method based on preference-approval structure and prospect theory. Computers & Industrial Engineering 2021, 158, 107349 .

AMA Style

Jie Long, Haiming Liang, Lei Gao, Zhaoxia Guo, Yucheng Dong. Consensus reaching with two-stage minimum adjustments in multi-attribute group decision making: A method based on preference-approval structure and prospect theory. Computers & Industrial Engineering. 2021; 158 ():107349.

Chicago/Turabian Style

Jie Long; Haiming Liang; Lei Gao; Zhaoxia Guo; Yucheng Dong. 2021. "Consensus reaching with two-stage minimum adjustments in multi-attribute group decision making: A method based on preference-approval structure and prospect theory." Computers & Industrial Engineering 158, no. : 107349.

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.

Journal article
Published: 26 February 2021 in Information Sciences
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Offline map matching is a crucial step to facilitate many trajectory-based services in urban areas by finding vehicles’ travel paths from recorded and stored trajectory data. This paper proposes a novel turning point-based offline map matching algorithm, which introduces the concept of vehicle turning points to implement map matching piecewisely. The algorithm first separates the entire trajectory into multiple sub-trajectories using the identified turning points. It then selects the best-matched path for each sub-trajectory from the corresponding K-shortest paths. Extensive experiments are conducted to compare the performance of our algorithm with five state-of-the-art map matching algorithms in terms of four different criteria, including one correctly matched criterion, two incorrectly matched criteria, and one computation time-related criterion. Experimental results show that our algorithm has the best average matching accuracy and efficiency at different sampling intervals. Specifically, compared with the five benchmark algorithms, our algorithm can improve the correctly matched percentages by 1.43% to 34.66%, reduce the incorrectly matched percentages by 15.23% to 56.79%, and improve the matching speeds by 3.16–61.01 times.

ACS Style

Dongqing Zhang; Yucheng Dong; Zhaoxia Guo. A turning point-based offline map matching algorithm for urban road networks. Information Sciences 2021, 565, 32 -45.

AMA Style

Dongqing Zhang, Yucheng Dong, Zhaoxia Guo. A turning point-based offline map matching algorithm for urban road networks. Information Sciences. 2021; 565 ():32-45.

Chicago/Turabian Style

Dongqing Zhang; Yucheng Dong; Zhaoxia Guo. 2021. "A turning point-based offline map matching algorithm for urban road networks." Information Sciences 565, no. : 32-45.

Journal article
Published: 14 February 2021 in Computers & Industrial Engineering
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Multiple attribute decision making (MADM) approaches have been investigated extensively for ranking the alternatives related to multiple attributes. In most existing MADM approaches, attribute weights play a key role due to the fact that the ranking of alternatives may changes with attribute weights vector. Ranking range can be used to measure the lower and upper bounds of all possible rankings of alternatives when attribute weights are varying. In the paper, we investigate the ranking range for the selected seven popular MADM approaches: Weighted averaging (WA), weighted geometric averaging (WGA), ordered weighted averaging (OWA), ordered weighted geometric averaging (OWGA), TOPSIS, PROMETHEE and ELECTRE. Through determining the ranking ranges of alternatives, associated with attribute weights, in the selected seven approaches, we present several desirable properties of the ranking range. Then, we design the simulation experiments with either random or real data to compare the ranking range for selected seven MADM approaches. Interestingly, the experiment results clearly show that TOPSIS≻ELECTRE≻PROMETHEE~WA≻WGA≻OWA≻OWGA in the average sense, where '≻' denotes the larger ranking range, and '~' denotes no difference in the ranking range. A larger ranking range means an easier to manipulate the ranking of alternatives, and also means a worse robustness of a MADM approach.

ACS Style

Yating Liu; Zhengwei Sun; Haiming Liang; Yucheng Dong. Ranking range model in multiple attribute decision making: A comparison of selected methods. Computers & Industrial Engineering 2021, 155, 107180 .

AMA Style

Yating Liu, Zhengwei Sun, Haiming Liang, Yucheng Dong. Ranking range model in multiple attribute decision making: A comparison of selected methods. Computers & Industrial Engineering. 2021; 155 ():107180.

Chicago/Turabian Style

Yating Liu; Zhengwei Sun; Haiming Liang; Yucheng Dong. 2021. "Ranking range model in multiple attribute decision making: A comparison of selected methods." Computers & Industrial Engineering 155, no. : 107180.

Journal article
Published: 15 January 2021 in IEEE Transactions on Cybernetics
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Inspired by the continuous opinion and discrete action (CODA) model, bounded confidence and social networks, the bounded confidence evolution of opinions and actions in social networks is investigated and a social network opinions and actions evolutions (SNOAEs) model is proposed. In the SNOAE model, it is assumed that each agent has a CODA for a certain issue. Agents' opinions are private and invisible, that is, an individual agent only knows its own opinion and cannot obtain other agents' opinions unless there is a social network connection edge that allows their communication; agents' actions are public and visible to all agents and impact other agents' actions. Opinions and actions evolve in a directed social network. In the limitation of the bounded confidence, other agents' actions or agents' opinions noticed or obtained by network communication, respectively, are used by agents to update their opinions. Based on the SNOAE model, the evolution of the opinions and actions with bounded confidence is investigated in social networks both theoretically and experimentally with a detailed simulation analysis. Theoretical research results show that discrete actions can attract agents who trust the discrete action, and make agents to express extreme opinions. Simulation experiments results show that social network connection probability, bounded confidence, and the opinion threshold of action choice parameters have strong impacts on the evolution of opinions and actions. However, the number of agents in the social network has no obvious influence on the evolution of opinions and actions.

ACS Style

Min Zhan; Gang Kou; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. Bounded Confidence Evolution of Opinions and Actions in Social Networks. IEEE Transactions on Cybernetics 2021, PP, 1 -12.

AMA Style

Min Zhan, Gang Kou, Yucheng Dong, Francisco Chiclana, Enrique Herrera-Viedma. Bounded Confidence Evolution of Opinions and Actions in Social Networks. IEEE Transactions on Cybernetics. 2021; PP (99):1-12.

Chicago/Turabian Style

Min Zhan; Gang Kou; Yucheng Dong; Francisco Chiclana; Enrique Herrera-Viedma. 2021. "Bounded Confidence Evolution of Opinions and Actions in Social Networks." IEEE Transactions on Cybernetics PP, no. 99: 1-12.

Journal article
Published: 31 December 2020 in Reliability Engineering & System Safety
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As a forward-looking reliability-management engineering technique, failure mode and effect analysis (FMEA) has been widely utilized to improve the reliability of products, processes, systems, and services. In practice, multiple responsible parties for FMEA implementation have different backgrounds, knowledge levels, and opinions. Integrating consensus into FMEA has some notable merits: the connections between FMEA participants can be strengthened, and a collective solution with a high degree of acceptability to the FMEA problem can be yielded. Meanwhile, the social network relationship among FMEA participants should be an essential element in FMEA because the participants’ opinions are subject to influence by each other and likely to evolve due to their social network interactions. Thus, this study first proposes a social network consensus model with minimum adjustment distance to assist FMEA participants in attaining a consensus, in which participants utilize a linguistic distribution assessment approach to represent their opinions. Second, an opinion evolution-based social network consensus model with minimum adjustment distance is further presented by considering the phenomenon of opinion evolution. Finally, some theoretical analyses, a case study, and a detailed comparative analysis are presented to verify the validity of the proposed FMEA approach.

ACS Style

Hengjie Zhang; Yucheng Dong; Jing Xiao; Francisco Chiclana; Enrique Herrera-Viedma. Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts. Reliability Engineering & System Safety 2020, 208, 107425 .

AMA Style

Hengjie Zhang, Yucheng Dong, Jing Xiao, Francisco Chiclana, Enrique Herrera-Viedma. Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts. Reliability Engineering & System Safety. 2020; 208 ():107425.

Chicago/Turabian Style

Hengjie Zhang; Yucheng Dong; Jing Xiao; Francisco Chiclana; Enrique Herrera-Viedma. 2020. "Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts." Reliability Engineering & System Safety 208, no. : 107425.

Journal article
Published: 24 December 2020 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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This article provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision, and/or use natural language to assess decision alternatives, criteria, etc. The aim of this article is threefold. First, the main fuzzy set theory and computing with words-based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Second, three core decision-making frameworks are examined: 1) multicriteria decision making; 2) group consensus-driven decision making; and 3) multiperson multicriteria decision making. Third, the article discusses new complex decision-making frameworks that have emerged in recent years, where decisions are guided by the “wisdom of the crowd”: their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided.

ACS Style

Enrique Herrera-Viedma; Ivan Palomares; Cong-Cong Li; Francisco Javier Cabrerizo; Yucheng Dong; Francisco Chiclana; Francisco Herrera. Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, 51, 191 -208.

AMA Style

Enrique Herrera-Viedma, Ivan Palomares, Cong-Cong Li, Francisco Javier Cabrerizo, Yucheng Dong, Francisco Chiclana, Francisco Herrera. Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020; 51 (1):191-208.

Chicago/Turabian Style

Enrique Herrera-Viedma; Ivan Palomares; Cong-Cong Li; Francisco Javier Cabrerizo; Yucheng Dong; Francisco Chiclana; Francisco Herrera. 2020. "Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way." IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, no. 1: 191-208.

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: 07 December 2020 in IEEE Transactions on Cybernetics
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In the analytic hierarchy process (AHP), the reciprocal matrix is generated based on the pairwise comparisons completed among all the alternatives or attributes under consideration. To ensure reliability and validity of the decision solution, a certain modification of entries of the matrix is usually needed to improve the consistency of the reciprocal matrix. This study aims to present a consistency improvement method by admitting some level of information granularity in the evaluation process. This gives rise to a granular rather than numeric matrix of pairwise comparisons. First, with a given average level of information granularity, we present an optimal granularity model that is characterized by maximal consistency. One can maximize the consistency degree by invoking a process of allocation of information granularity across the corresponding modifications of the reciprocal matrix. Based on the optimal granularity model, an interactive consistency improvement process is presented with the involvement of the decision maker. Then, an adaptive differential evolution algorithm is applied to optimize entries of the modified reciprocal matrix. Detailed experiments along with a thorough comparative analysis are completed to demonstrate the effectiveness of the proposed method.

ACS Style

Bowen Zhang; Witold Pedrycz; Aminah Robinson Fayek; Yucheng Dong. A Differential Evolution-Based Consistency Improvement Method in AHP With an Optimal Allocation of Information Granularity. IEEE Transactions on Cybernetics 2020, PP, 1 -12.

AMA Style

Bowen Zhang, Witold Pedrycz, Aminah Robinson Fayek, Yucheng Dong. A Differential Evolution-Based Consistency Improvement Method in AHP With an Optimal Allocation of Information Granularity. IEEE Transactions on Cybernetics. 2020; PP (99):1-12.

Chicago/Turabian Style

Bowen Zhang; Witold Pedrycz; Aminah Robinson Fayek; Yucheng Dong. 2020. "A Differential Evolution-Based Consistency Improvement Method in AHP With an Optimal Allocation of Information Granularity." IEEE Transactions on Cybernetics PP, no. 99: 1-12.

Journal article
Published: 10 November 2020 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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In computing with words, it has been stressed that words mean different things for different people, which entails that decision makers (DMs) have personalized individual semantics (PISs) attached to linguistic expressions in linguistic group decision making (GDM). In particular, the PISs of DMs are not fixed, and they will be changing during the consensus building process, which indicates the necessary of continual PIS learning. Therefore, in this article, we propose a continual PIS-learning-based consensus approach in linguistic GDM. Specifically, a continual PIS learning model with the consistency-driven methodology is proposed to update the PISs taking into account all the linguistic preference data given by DMs during the consensus process. Then, the consensus measurement and feedback recommendation based on PIS are developed to detect the consensus process. Finally, numerical examples and simulation analysis are presented to illustrate and justify the use of the continual PIS-learning-based consensus approach.

ACS Style

Cong-Cong Li; Yucheng Dong; Witold Pedrycz; Francisco Herrera. Integrating Continual Personalized Individual Semantics Learning in Consensus Reaching in Linguistic Group Decision Making. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, PP, 1 -12.

AMA Style

Cong-Cong Li, Yucheng Dong, Witold Pedrycz, Francisco Herrera. Integrating Continual Personalized Individual Semantics Learning in Consensus Reaching in Linguistic Group Decision Making. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020; PP (99):1-12.

Chicago/Turabian Style

Cong-Cong Li; Yucheng Dong; Witold Pedrycz; Francisco Herrera. 2020. "Integrating Continual Personalized Individual Semantics Learning in Consensus Reaching in Linguistic Group Decision Making." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-12.

Article
Published: 03 October 2020 in Group Decision and Negotiation
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Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. Detailed numerical and comparison analyses have been proposed to justify our proposal.

ACS Style

Cong-Cong Li; Yuan Gao; Yucheng Dong. Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making. Group Decision and Negotiation 2020, 30, 97 -118.

AMA Style

Cong-Cong Li, Yuan Gao, Yucheng Dong. Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making. Group Decision and Negotiation. 2020; 30 (1):97-118.

Chicago/Turabian Style

Cong-Cong Li; Yuan Gao; Yucheng Dong. 2020. "Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making." Group Decision and Negotiation 30, no. 1: 97-118.

Journal article
Published: 25 August 2020 in Decision Support Systems
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A rating system (RS) comprises a rating metric defined by a discrete set of integers contained in an interval (e.g., [0, N]), and an aggregation rule. RSs are widely used in various fields to capture and summarize individuals' opinions on alternatives. In this paper we argue that the multi-attribute additive value model (MAVM) should be used as a benchmark to analyze the reliability of RSs, and present some tight bounds on the parameter N and overall rating scores, which guarantee the consistency between the RS [0, N] and MAVM at the ranking and rating levels. Interestingly, the tight bounds at the rating level are twice as large as those at the ranking level. The results in this paper can provide new insights about the reliability analysis of RSs.

ACS Style

Sihai Zhao; Yucheng Dong; Ying He. The reliability analysis of rating systems in decision making: When scale meets multi-attribute additive value model. Decision Support Systems 2020, 138, 113384 .

AMA Style

Sihai Zhao, Yucheng Dong, Ying He. The reliability analysis of rating systems in decision making: When scale meets multi-attribute additive value model. Decision Support Systems. 2020; 138 ():113384.

Chicago/Turabian Style

Sihai Zhao; Yucheng Dong; Ying He. 2020. "The reliability analysis of rating systems in decision making: When scale meets multi-attribute additive value model." Decision Support Systems 138, no. : 113384.

Editorial
Published: 21 August 2020 in Information Fusion
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Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.

ACS Style

Yuzhu Wu; Zhen Zhang; Gang Kou; Hengjie Zhang; Xiangrui Chao; Cong-Cong Li; Yucheng Dong; Francisco Herrera. Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence. Information Fusion 2020, 65, 165 -178.

AMA Style

Yuzhu Wu, Zhen Zhang, Gang Kou, Hengjie Zhang, Xiangrui Chao, Cong-Cong Li, Yucheng Dong, Francisco Herrera. Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence. Information Fusion. 2020; 65 ():165-178.

Chicago/Turabian Style

Yuzhu Wu; Zhen Zhang; Gang Kou; Hengjie Zhang; Xiangrui Chao; Cong-Cong Li; Yucheng Dong; Francisco Herrera. 2020. "Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence." Information Fusion 65, no. : 165-178.

Journal article
Published: 20 August 2020 in IEEE Transactions on Fuzzy Systems
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Analytic hierarchy process (AHP) is widely employed to guide the decision maker to rank or evaluate the alternatives in decision activities. Its fuzzy set-based version, viz. the fuzzy AHP, has also been widely studied and applied since its inception. The essential distinction between the AHP and fuzzy AHP comes from the diverse transformation methods between the linguistic and numeric judgements. In this study, we conduct a thorough comparative study between the AHP and fuzzy AHP methods in the framework of two linguistic models, viz. linguistic model based on membership functions and 2-tuple linguistic model. Firstly, four AHP and three fuzzy AHP methods are revisited with the involvement of two linguistic models. Then, the comparison criteria are involved by calculating the cardinal or ordinal deviation between the original information and decision solutions, and the effects of the transitivity of the reciprocal matrix are also discussed in the comparative study. Finally, detailed experiments along with a thorough comparative analysis are conducted based on the random and publicly available data to show the difference between the AHP and fuzzy AHP methods.

ACS Style

Bowen Zhang; Cong-Cong Li; Yucheng Dong; Witold Pedrycz. A Comparative Study Between Analytic Hierarchy Process and Its Fuzzy Variants: A Perspective based on Two Linguistic Models. IEEE Transactions on Fuzzy Systems 2020, PP, 1 -1.

AMA Style

Bowen Zhang, Cong-Cong Li, Yucheng Dong, Witold Pedrycz. A Comparative Study Between Analytic Hierarchy Process and Its Fuzzy Variants: A Perspective based on Two Linguistic Models. IEEE Transactions on Fuzzy Systems. 2020; PP (99):1-1.

Chicago/Turabian Style

Bowen Zhang; Cong-Cong Li; Yucheng Dong; Witold Pedrycz. 2020. "A Comparative Study Between Analytic Hierarchy Process and Its Fuzzy Variants: A Perspective based on Two Linguistic Models." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Journal article
Published: 16 July 2020 in Scientific Reports
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Link travel speeds in road networks are essential data for a variety of research problems in logistics, transportation, and traffic management. Real-world link travel speeds are stochastic, and highly dependent on speeds in previous time periods and neighboring road links. To understand how link travel speeds vary over space and time, we uncover their distributions, their space- and/or time-dependent correlations, as well as partial correlations, based on link travel speed datasets from an urban road network and a freeway network. We find that more than 90% (57%) of travel speeds are normally distributed in the urban road (freeway) network, and that correlations generally decrease with increased distance in time and space. We also investigate if and how different types of road links affect marginal distributions and correlations. The results show that different road link types produce quite similar marginal distributions and correlations. Finally, we study marginal distributions and correlations in a freeway network. Except that the marginal distribution and time correlation are different from the urban road network, others are similar.

ACS Style

Feng Guo; Xin Gu; Zhaoxia Guo; Yucheng Dong; Stein W. Wallace. Understanding the marginal distributions and correlations of link travel speeds in road networks. Scientific Reports 2020, 10, 1 -8.

AMA Style

Feng Guo, Xin Gu, Zhaoxia Guo, Yucheng Dong, Stein W. Wallace. Understanding the marginal distributions and correlations of link travel speeds in road networks. Scientific Reports. 2020; 10 (1):1-8.

Chicago/Turabian Style

Feng Guo; Xin Gu; Zhaoxia Guo; Yucheng Dong; Stein W. Wallace. 2020. "Understanding the marginal distributions and correlations of link travel speeds in road networks." Scientific Reports 10, no. 1: 1-8.

Journal article
Published: 25 June 2020 in Information Sciences
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The axiomatic distance-based method is a powerful tool to aggregate individual preferences, and the extant axiomatic distance-based aggregation methods are with regard to individual numerical preferences. However, in some real-world decision problems with qualitative aspects, it is more convenient and natural for individuals to express their preferences through linguistic terms rather than through numerical values. Therefore, in this paper, we propose axiomatic distance in the linguistic context based on ordered linguistic term sets to aggregate individual linguistic preferences. Specifically, we provide some natural axioms on the distance measure among linguistic preferences. We then prove that there exists a unique distance function that satisfies all the proposed axioms. Based on the axiomatic distance function, we aggregate individual linguistic preferences into the group linguistic preference, which minimizes the total distance among individual linguistic preferences. Furthermore, we present a novel consensus measure based on the unique axiomatic distance and develop a minimum cost consensus model to obtain the optimal adjusted linguistic preference, which serves as a reference for the moderator to persuade individuals to modify their linguistic preferences.

ACS Style

Yao Li; Xia Chen; Yucheng Dong; Francisco Herrera. Linguistic group decision making: Axiomatic distance and minimum cost consensus. Information Sciences 2020, 541, 242 -258.

AMA Style

Yao Li, Xia Chen, Yucheng Dong, Francisco Herrera. Linguistic group decision making: Axiomatic distance and minimum cost consensus. Information Sciences. 2020; 541 ():242-258.

Chicago/Turabian Style

Yao Li; Xia Chen; Yucheng Dong; Francisco Herrera. 2020. "Linguistic group decision making: Axiomatic distance and minimum cost consensus." Information Sciences 541, no. : 242-258.

Journal article
Published: 16 May 2020 in European Journal of Operational Research
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In group decision making, the interaction behaviors between the moderator and decision makers play a critical role in a consensus process. In this study, based on the essential architecture of Stackelberg game, we present a bi-level optimization model to describe the interaction behaviors between decision makers and moderator, and develop the consensus mechanism with maximum-return modifications and minimum-cost feedback (MRMCCM). In the MRMCCM, the moderator aims to guide decision makers to reach consensus with minimum cost, while decision makers modify their own opinions based on the maximization of individual return. We analyze the equilibrium strategy in the MRMCCM, including the modification and compensation strategies composed of the optimal suggested opinion and unit consensus cost. In addition, an adaptive differential evolution is presented to deal with the bi-level optimization model, and the detailed experimental studies are conducted to justify the performance of the MRMCCM.

ACS Style

Bowen Zhang; Yucheng Dong; Hengjie Zhang; Witold Pedrycz. Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory. European Journal of Operational Research 2020, 287, 546 -559.

AMA Style

Bowen Zhang, Yucheng Dong, Hengjie Zhang, Witold Pedrycz. Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory. European Journal of Operational Research. 2020; 287 (2):546-559.

Chicago/Turabian Style

Bowen Zhang; Yucheng Dong; Hengjie Zhang; Witold Pedrycz. 2020. "Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory." European Journal of Operational Research 287, no. 2: 546-559.