Article
Computer Science, Artificial Intelligence
Yuzhu Wu, Yuan Gao, Bowen Zhang, Witold Pedrycz
Summary: This paper proposes a minimum information-loss transformation framework to support the useful fusion of heterogeneous distributed information in linguistic group decision making. By defining distributed linguistic distance measurements, the information loss among heterogeneous distributed linguistic preference information can be measured, and several minimum information-loss transformation models are proposed. The flexibility of distributed linguistic information is studied through numerical examples and comparative analyses to justify the effectiveness of the proposed models.
INFORMATION FUSION
(2023)
Article
Hospitality, Leisure, Sport & Tourism
Dan Wang, Iis Tussyadiah, Elaine Zhang
Summary: This study adopted sociomateriality as a theoretical tool to examine the interactions between travelers and their smartphones in an in-destination group decision-making context. Three communication patterns in group decision-making during trips were identified, along with four features of smartphone sociomateriality: knowledgeability, thriftiness, referenceability, and negotiability.
JOURNAL OF TRAVEL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Yi Liu, Guiwu Wei, Haobin Liu, Lei Xu
Summary: The study aims to construct the emergency group decision-making model for multiple network public opinion emergencies under the linguistic intuitionistic environment. New concepts such as extended Copula and extended Co-Copula are introduced, along with operational rules based on linguistic intuitionistic fuzzy numbers. The proposed EGDM approach integrates Choquet integral and operational rules of LIFNs, showing its validity and advantages compared to existing approaches.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Plant Sciences
Qiuran Wang, Silvia Guerra, Bianca Bonato, Valentina Simonetti, Maria Bulgheroni, Umberto Castiello
Summary: Finding a suitable support is crucial for climbing plants, as it affects their performance and fitness. Previous studies have focused on the mechanistic details of support-searching and attachment, while fewer have considered the ecological significance and influencing factors. This study investigates the influence of support diameter on pea plants' movement and reveals a preference for thinner supports. These findings shed further light on how climbing plants make decisions regarding support-searching and demonstrate their ability to adapt to environmental scenarios.
Article
Green & Sustainable Science & Technology
Amir Reza Bakhshi Lomer, Mahdi Rezaeian, Hamid Rezaei, Akbar Lorestani, Naeim Mijani, Mohammadreza Mahdad, Ahmad Raeisi, Jamal Jokar Arsanjani
Summary: This study presents a novel risk-based decision support system that helps disaster risk management planners select the best locations for emergency shelters after an earthquake. The system identifies important criteria based on stakeholder analysis, determines their weights through a Large Group Decision-Making (LGDM) model, and assesses the suitability of different locations using the Ordered Weighted Average (OWA) method. Factors such as distance from the fault, population density, access to green spaces, and building quality were found to be significant in selecting the best emergency shelters.
Article
Automation & Control Systems
Guangxu Li, Gang Kou, Yi Peng
Summary: As the number of participants in decision-making increases, the complexity of the group decision-making process also increases. Traditional methods divide large groups into smaller ones and translate heterogeneous information into a uniform format. This article uses fuzzy cluster analysis to integrate heterogeneous information for large-scale GDM problems and develops a feedback mechanism to adjust decision matrices when consensus cannot be reached.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Shahid Hussain Gurmani, Zhao Zhang, Rana Muhammad Zulqarnain, Sameh Askar
Summary: This paper proposes a methodology for supplier selection of emergency medical supplies based on the interaction and feedback mechanism (IFM) and T-spherical fuzzy sets (T-SFS). It introduces the T-SF partitioned Bonferroni mean (T-SFPBM) and T-SF weighted partitioned Bonferroni mean (T-SFWPBM) operators to fuse the evaluation information provided by experts, and utilizes IFM to achieve consensus among multiple experts. The weights of experts are determined using T-SF information. An MAGDM algorithm is designed based on the combination of IFM and T-SFWPBM operator. The suggested approach is demonstrated with an example of supplier selection for emergency medical supplies, and its reliability and accuracy are confirmed through analysis of parameter influence and comparison with existing methods.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Xuanhua Xu, Junyi Chai, Xiaohong Chen
Summary: This paper addresses the challenges of large-scale group consensus and decision-making under risk and emergency conditions in Group Decision Making (GDM). A problem-solving approach is proposed, which includes a feasible mechanism of group consensus strategies and a subgroup identification method. The approach is illustrated through a real case study and shows effective and efficient decision-making in complex large-scale GDM under risk and emergency.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Cao, Xuanhua Xu, Xuanpeng Yin, Bin Pan
Summary: This study proposes a decision-making method based on topic sentiment analysis to address the problem of completely data-driven attribute information acquisition and risk control in large group emergency decision-making. The method uses topic mining and sentiment analysis to obtain attribute system structure and weight information, and measures risk using risk credibility. A risk-consensus feedback mechanism is also designed to obtain high-consensus and low-risk alternatives.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Industrial
Xuanpeng Yin, Xuanhua Xu, Bin Pan
Summary: Different strategies can be applied in large group emergency decision-making, and reasonable decision-making strategy selection can effectively control decision-making risk. By comparing the decision-making risk degree of each decision-making strategy under different conditions, universal rules for strategy selection can be obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Environmental
Menghua Yang, Hong Chen, Ruyin Long, Yujie Wang, Qingqing Sun
Summary: Promoting green consumption behavior is an effective means for sustainable development. This study uses overreaction theory to analyze the irrational decisions of residents and provides theoretical references for policy formulation.
RESOURCES CONSERVATION AND RECYCLING
(2023)
Article
Computer Science, Artificial Intelligence
Joao Carneiro, Patricia Alves, Goreti Marreiros, Paulo Novais
Summary: There is a paradigm shift in decision-making towards group decisions, but it is becoming increasingly difficult for decision-makers to gather in one place at one time. Web-based Group Decision Support Systems aim to address this issue, but their inadequate definition has hindered their success. This study identifies challenges and potential barriers to acceptance by organizations, and proposes a conceptual definition for a Web-based Group Decision Support System. Various crucial topics, including communication and perception, are essential for supporting dispersed decision-makers, but often overlooked. Limitations still exist in terms of models and applications, preventing the development of higher quality systems.
Article
Automation & Control Systems
Jiayao Shen, Sha Wang, Feixia Ji, Tiantian Gai, Jian Wu
Summary: This study investigates the reconciliation mechanism to manage non-cooperative behaviors in social network groups using a cooperative intention index. Different types of non-cooperative behaviors are identified and reconciliation mechanisms are proposed accordingly.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Mingjun Jiang
Summary: Emergency decision-making problems often involve experts from different backgrounds who exhibit non-cooperative behaviors during the consensus-reaching process. Previous studies focused on experts' maximum cooperation as accepting revisions, limiting contributions by altruistic individuals. Additionally, existing studies grouped experts based on evaluation similarity or trust separately, without considering both factors simultaneously. This study introduces a clustering method considering evaluation similarity and trust relations and develops a consensus model incorporating experts' altruistic behaviors. Numerical results demonstrate the effectiveness of the proposed method, highlighting the importance of integrating altruistic behavior analysis to safeguard expert interests and decision-making integrity.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Bing Yan, Yanjun Wang, Wei Xia, Xiaoxuan Hu
Summary: This study proposes a novel consensus-reaching model in the social network environment for large-group emergency decision-making (LGEDM), which addresses non-cooperative behaviors. The model combines social network analysis and the modularity-based Louvain clustering algorithm to cluster decision makers and reduce complexity. A hierarchical feedback adjustment mechanism is introduced to manage non-cooperative behaviors without changing the clustering structure. A case study demonstrates the feasibility of the proposed model and comparative analysis showcases its superiority in clustering and managing non-cooperative behaviors in LGEDM.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Interdisciplinary Applications
Wei Liang, Alvaro Labella, Ying-Ming Wang, Rosa M. Rodriguez
Summary: This paper introduces a consensus reaching process (CRP) that smooths disagreements between experts in a multi-criteria group decision-making problem to reach a solution of consensus. The experts' preferences are modeled using interval-valued hesitant fuzzy sets (IVHFS) to handle information scarcity and uncertainty. The proposal utilizes an extension of the ordered weight averaging operator under an IVHFS for reasonable aggregations. The CRP also includes a feedback mechanism to provide individual suggestions for increasing agreement within the group. The proposal is validated through a practical study on renewable energy selection in China under various scenarios.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jinxing Zhu, Xueling Ma, Luis Martinez, Jianming Zhan
Summary: This article proposes a probabilistic linguistic three-way decision (TWD) method based on the regret theory (RT) for multiattribute decision-making (MADM) problems with probabilistic linguistic term sets (PLTSs). The method includes a probabilistic linguistic attribute weight determination method, an extended fuzzy c-means (FCM) algorithm for PLTSs, and the introduction of RT into PLTSs. The effectiveness and superiority of the method are verified through comparative and sensitivity analysis.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Muhammet Deveci, Rosa M. Rodriguez, Dragan Pamucar, Madjid Tavana, Harish Garg
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jianming Zhan, Jiang Deng, Zeshui Xu, Luis Martinez
Summary: This article introduces the combination of three-way decision theory and regret theory, proposes a novel method for estimating incomplete utility values, and establishes a wide sense three-way decision model for incomplete multiscale decision information systems. The feasibility, validity, and stability of the model are verified through experiments and parametric analyses.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Peng Wu, Fengen Li, Jie Zhao, Ligang Zhou, Luis Martinez
Summary: This article proposes a new method for large-scale group decision making (LSGDM) that includes a clustering algorithm, a weight determination method, and a consensus reaching process. The DMs are classified into different subgroups based on the consensus and consistency of the additive preference relation, and a weight determination method based on cooperative game theory is proposed. The consensus of each subgroup is measured using intra- and interconsensus levels, and different feedback mechanisms are presented based on multi-objective optimization models for different consensus reaching scenarios.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jian-Peng Chang, Zhen-Song Chen, Zhu-Jun Wang, LeSheng Jin, Witold Pedrycz, Luis Martinez, Miroslaw J. Skibniewski
Summary: This article focuses on assessing the spatial synergy between the urban rail transit (URT) network and its feeder transit system by developing a novel multicriteria large-scale group assessment (MCLSGA) model. The model uses uncertain linguistic information and clustering algorithm to process subjective assessment information and control the weighting of criteria. A case study is conducted to validate the validity of the proposed model.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zi-Xin Zhang, Liang Wang, Ying-Ming Wang, Luis Martinez
Summary: This study proposes a novel alpha level sets based fuzzy DEMATEL method to handle fuzzy information and considers experts' hesitation under uncertain and fuzzy environment. The proposed method improves the existing fuzzy DEMATEL studies and enriches the theoretical studies of fuzzy DEMATEL.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Diego Garcia-Zamora, Alvaro Labella, Rosa M. Rodriguez, Luis Martinez
Summary: Linguistic group decision making (LiGDM) is a method that aims to solve decision scenarios involving human decision makers by using linguistic information. Linguistic consensus reaching processes (Li-CRPs) have been developed to reach agreed solutions that increase satisfaction. However, there is a lack of objective metrics to compare these models and evaluate their performance. This article introduces a metric based on a linguistic comprehensive minimum cost consensus (CMCC) model, which considers consensus degree and the cost of modifying initial opinions.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Guangquan Huang, Liming Xiao, Witold Pedrycz, Genbao Zhang, Luis Martinez
Summary: This study proposes an enhanced FMEA model using T-spherical fuzzy sets, which allows for flexible expression of experts' preferences, improves the rationality of evaluation results, and addresses the issues of weight allocation and stability in risk ranking.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Information Systems
Yaya Liu, Haifeng Zhou, Rosa M. Rodriguez, Luis Martinez
Summary: In order to incorporate linguistic information into decision making, it is necessary to apply computing with words (CW) techniques. Recent researches have partially overcome the limitation of traditional models by using comparative linguistic expressions. However, the flexibility of linguistic information expression remains limited due to the discrete distributions of primary terms.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Ilgin Gokasar, Dragan Pamucar, Muhammet Deveci, Brij B. Gupta, Luis Martinez, Oscar Castillo
Summary: Continuous, efficient, and sustainable collection of traffic data can be achieved through the use of self-powered sensors once connected autonomous vehicles (CAVs) are integrated into metaverse technology. The integration of metaverse self-powered sensors allows for uninterrupted data capture, enabling activities such as traffic network management, transportation facility optimization, and urban and intercity journey management. Additionally, the study of transportation systems in conjunction with the metaverse can enhance transportation efficiency and sustainability. This study prioritizes four alternatives of CAVs in the metaverse using a novel decision-making model that incorporates self-powered sensors.
INFORMATION SCIENCES
(2023)
Proceedings Paper
Management
Alvaro Labella, Diego Garcia-Zamora, Rosa M. Rodriguez, Luis Martinez
Summary: In order to address the inconsistencies in Multi-criteria Decision-Making (MCDM) problems, an extension of the Best-Worst Method (BWM) is proposed for multi-criteria group decision-making (MCGDM) problems. An optimization model based on linear programming is introduced to handle disagreements among decision-makers and obtain consensual solutions.
ADVANCES IN BEST-WORST METHOD, BWM2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Xia Wang, Jun Liu, Chris D. Nugent, Samuel J. Moore, Yang Xu
Summary: With the rapid development of IoT, Smart Home has gained popularity as an application market. The present work proposes a data-knowledge integrated solution to analyze the reliability of sensor systems in a smart home. Probabilistic model checking techniques are used to check quantitative properties and provide mathematical guarantee for them.
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022)
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Francisco Moya, Luis Martinez, Fco Javier Estrella
Summary: Modern intelligent health-based software relies on data-driven systems that collect data from wearable devices for continuous remote monitoring of patient parameters. The significance of wearable devices and data streams lies in their cost efficiency and ability to provide numerous solutions to health-based problems. However, the security of data in these applications is crucial, which is where Distributed Ledger Technologies (DLT) come into play by offering resistance to information manipulation and resilience to single points of failure.
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022)
(2023)
Article
Computer Science, Artificial Intelligence
Kuo Pang, Luis Martinez, Nan Li, Jun Liu, Li Zou, Mingyu Lu
Summary: This paper proposes a MAGDM approach based on linguistic concept lattices, which is effective and rational in aggregating the opinions of multiple individuals during the decision-making process. By constructing linguistic concept lattices and introducing the extent of fuzzy linguistic concepts and meet-irreducible elements, information loss can be reduced and the rationality of decision results can be enhanced.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
