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Öğe Aggregation of Basic Uncertain Information With Two-Step Aggregation Frame(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Jin, Lesheng; Chen, Zhen-Song; Pedrycz, Witold; Senapati, Tapan; Yatsalo, Boris; Mesiar, Radko; Beliakov, GlebThere exist various categories of uncertain information, and their corresponding methods of aggregation may also vary. At present, there exists a dearth of specifically tailored techniques for aggregating basic uncertain information (BUI). The present study introduces a two-step aggregation frame that is applicable to inputs of both real-valued and BUI-valued inputs. In the process of constructing such a frame, several novel notions and definitions are introduced. These comprise of extended aggregation operators with respect to a finite set and to a collection of subsets of the set, some certainty independent BUI aggregation and some certainty dependent BUI aggregation, BUI merging operators and BUI aggregation operators, BUI-valued min operator, and BUI-valued Sugeno integral. Some corresponding deductions, necessary reasoning and numerical examples are presented.Öğe Dynamic collective opinion generation framework for digital transformation barrier analysis in the construction industry(Elsevier, 2024) Chen, Zhen-Song; Liang, Chong-Ze; Xu, Ya-Qiang; Pedrycz, Witold; Skibniewski, Miroslaw J.The absence of a reliable, dynamic evaluation system has impeded early-stage industrial research progress, particularly in the digital transformation of the construction industry. Moreover, existing research studies rarely explore the impact of digitalt transformation barriers considering the interplays among them. This paper aims to introduce an innovative framework to generate dynamic collective opinions for barrier analysis in such context. The proposed dynamic collective opinion generation framework comprises three key components: Collective Opinion Generation, Prediction with Expert Advice (PEA), and Social Network Analysis. Its goal is to provide dependable decision support when subjective evaluation data from experts is available. Initially, a bi-objective optimization model generates the initial barrier weight vector. The PEA incorporates a loss function to measure the deviation between aggregated probablity density function and actual observed data, updating the weight vector over time. Next, an influence network covering all barriers is established. Node significance is evaluated through metrics like degree centrality, closeness centrality, and eigenvector centrality. The gravity model based on three metrics is used to determine interrelationships among barriers, resulting in a weight vector capturing these interplays. The two weight vectors are combined with Nash equilibrium, yielding the ultimate weight vector for barriers. The effectiveness of the proposed dynamic collective opinion generation framework is showcased through a case study on China Construction Third Bureau. Results indicate that talent structure notably influences construction companies' digital transformation. Additionally, market structure and strategic position significantly impact digital transformation in this industry.Öğe Enhancing the sustainability and robustness of critical material supply in electrical vehicle market: an AI-powered supplier selection approach(Springer, 2023) Wang, Zhu-Jun; Chen, Zhen-Song; Su, Qin; Chin, Kwai-Sang; Pedrycz, Witold; Skibniewski, Miroslaw J.In light of the burgeoning electric vehicle market, the demand for lithium-ion batteries (LiBs) is on the rise. However, the supply of materials essential for LiBs is struggling to keep pace, posing a significant challenge in meeting the surging market demand. This study offers a viable solution to bolster the dependability of the material supply chain by prioritizing material suppliers who are deeply committed to sustainable practices and performance. We have developed a comprehensive system for evaluating sustainable performance, encompassing three vital dimensions: economic, social and environmental contexts. Then, we introduced a pioneering approach known as the multi-criteria material supplier selection (MCMSS) methodology which amalgamates multi-criteria decision-making techniques with artificial intelligence to effectively generate sustainability performance of suppliers and identify the most suitable supplier, out of all alternatives. Eventually, the supply of four key materials of LiBs is used as illustrative examples to verify the feasibility and rationality of the proposed MCMSS. This work carries significant implications for overseeing the LiB material industry. The MCMSS model offers a solution for the government to establish a comprehensive material supplier database to intelligently supervise the activities of material suppliers and foster collaboration between upstream and downstream enterprises within the LiB industry.Öğe Evaluating Holistic Privacy Risk Posed by Smart Home Ecosystem: A Capability-Oriented Model Accommodating Epistemic Uncertainty and Wisdom of Crowds(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Chang, Jian-Peng; Zheng, Hong-Liang; Mardani, Abbas; Pedrycz, Witold; Chen, Zhen-SongEvaluating the holistic privacy risk (HPR) presented by a smart home ecosystem (SHE), encompassing both internal and external entities that may be targeted by different adversaries seeking to compromise users' privacy, can enhance the comprehensive understanding of the privacy risk landscape within the SHE. This matter is influenced by the complexity of risk surroundings, the diverse perspectives of users toward privacy, and the lack of historical data. Unfortunately, existing literature falls short in addressing these factors. To fill the gap, this article develops an innovative capability-oriented model that accommodates epistemic uncertainty and wisdom of crowds (WoC), designed to assist smart home device manufacturers in accurately assessing HPR posed by their SHEs. The model presents a method for representing subjective judgments that captures epistemic uncertainty and a technique for weighting individual judgments to mitigate overconfidence bias, thus effectively harnessing WoC. In addition, this model features two specialized methods: one for quantifying HPR and another for prioritizing associated single risks, both tailored to operate effectively within uncertain context. These innovative methods are versatile and can be applied to various risk assessment scenarios, especially where historical data are not available. The practicality and effectiveness of our model are demonstrated through a detailed case study.Öğe Identifying Digital Transformation Barriers in Small and Medium-Sized Construction Enterprises: A Multi-criteria Perspective(Springer, 2024) Chen, Zhen-Song; Lu, Jing-Yi; Wang, Xian-Jia; Pedrycz, WitoldThe digitalization of small and medium-sized enterprises within the construction sector is significantly limited as a result of their distinct characteristics. Despite the potential for significant and enlightening research, there has been an inadequate concentration on identifying the factors that impede the rapid progress of digitalization in small and medium-sized construction enterprises (SMCEs). The objective of this study is to examine the barriers hindering the digital transformation of SMCEs and provide a framework for assessing and prioritizing these barriers. Therefore, this research endeavors to design a barrier indicator system specifically tailored to the digital transformation of SMCEs. Based on this, a novel decision support model has been proposed within the context of basic uncertain linguistic information (BULI) by integrating the three-way decision (TWD) model and the consensus reaching process framework. The suggested model adopts the concept of BULI to effectively represent and manage uncertain information. It further offers the BULI-based TWD model, which is designed to categorize the relevance of barriers. In addition, the model suggests the BULI-based minimal adjustment consensus model, which aims to enhance the degree of consensus. The model's usefulness is confirmed by its application in evaluating barriers to digital transformation in an SMCE located in Wuhan, China. Furthermore, the sensitivity and comparative analyses are conducted to illustrate the benefits of the proposed model in comparison to existing approaches. The proposed model expands upon the existing theory and practical implementation of the TWD method. It also offers a valuable approach for devising a barrier response strategy that can be applied to the digital transformation of SMCEs.Öğe Large-group failure mode and effects analysis for risk management of angle grinders in the construction industry(Elsevier, 2023) Chen, Zhen-Song; Chen, Jun-Yang; Chen, Yue-Hua; Yang, Yi; Jin, LeSheng; Herrera-Viedma, Enrique; Pedrycz, WitoldAccidents associated with the use of construction equipment are among the leading causes of fatal injuries in the construction industry. In particular, angle grinders are associated with a significant number of occupational injuries every year. However, practitioners and researchers have paid limited attention to this issue. To facilitate the development of more sophisticated plans and guidelines to prevent angle grinder-related accidents, failure mode and effects analysis (FMEA) is employed for risk management in such a context. The conventional FMEA method is extensively used for examining potential failure in many industries, but has been criticized much in the literature for its various limitations. This study presents a novel large-group FMEA (LGFMEA) model, which integrates a clustering analysis for handling experts at large scale, a consensus reaching process with relative basic uncertain linguistic information (RBULI) to manage opinion diversity among experts, and the behavioral TOPSIS method to rank failure modes. The assessment information is characterized by the RBULI, a novel information representation construction method applied to handle complex evaluations under uncertainty. Finally, the proposed LGFMEA approach is performed for risk analysis related to angle grinder use to obtain insights into risk mitigation, and the sensitive and comparative analyses are performed to verify the rationality and feasibility of the model.Öğe Multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network(Springer Heidelberg, 2023) Mu, Nengye; Wang, Yuanshun; Chen, Zhen-Song; Xin, Peiyuan; Deveci, Muhammet; Pedrycz, WitoldThe recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost.Öğe Multiobjective optimization-based decision support for building digital twin maturity measurement(Elsevier Sci Ltd, 2024) Chen, Zhen-Song; Chen, Kou-Dan; Xu, Ya-Qiang; Pedrycz, Witold; Skibniewski, Miroslaw J.The digital twin (DT) represents a powerful tool for advancing construction industry to provide a cyber- physical integration that enables real-time monitoring of assets and activities and facilitates decision-making. Due to the inherent characteristics of the construction industry and the diverse possibilities with DT, proliferation of building digital twin (BDT) necessitates a comprehensive comprehension of its evolution and the creation of roadmaps. This paper aims to contribute to the formalization and standardization of BDT. It designs a novel assessment framework for the overall maturity measurement of existing BDT projects. The developed BDT maturity model incorporates a collective opinion generation paradigm based on a fairness aware multiobjective optimization model to provide an expert-based evaluation system for evaluating the maturity of BDT projects. The effectiveness and feasibility of the proposed framework have been validated through a case study of an experimental BDT initiative. This paper establishes a generalizable framework for BDT maturity assessment that can offer insights into BDT maturity standards to construction practitioners to create effective strategies for the diffusion, development, and maturation of BDT.Öğe NONPARAMETRIC NUMERICAL APPROACHES TO PROBABILITY WEIGHTING FUNCTION CONSTRUCTION FOR MANIFESTATION AND PREDICTION OF RISK PREFERENCES(Vilnius Gediminas Tech Univ, 2023) Wu, Sheng; Chen, Zhen-Song; Pedrycz, Witold; Govindan, Kannan; Chin, Kwai-SangProbability weighting function (PWF) is the psychological probability of a decision-maker for ob-jective probability, which reflects and predicts the risk preferences of decision-maker in behavioral decision-making. The existing approaches to PWF estimation generally include parametric methodologies to PWF con-struction and nonparametric elicitation of PWF. However, few of them explores the combination of parametric and nonparametric elicitation approaches to approximate PWF. To describe quantitatively risk preferences, the Newton interpolation, as a well-established mathematical approximation approach, is introduced to task-specifi-cally match PWF under the frameworks of prospect theory and cumulative prospect theory with descriptive psy-chological analyses. The Newton interpolation serves as a nonparametric numerical approach to the estimation of PWF by fitting experimental preference points without imposing any specific parametric form assumptions. The elaborated nonparametric PWF model varies in accordance with the number of the experimental preference points elicitation in terms of its functional form. The introduction of Newton interpolation to PWF estimation into decision-making under risk will benefit to reflect and predict the risk preferences of decision-makers both at the aggregate and individual levels. The Newton interpolation-based nonparametric PWF model exhibits an inverse S-shaped PWF and obeys the fourfold pattern of decision-makers' risk preferences as suggested by previous empirical analyses.Öğe Optimization-based probabilistic decision support for assessing building information modelling (BIM) maturity considering multiple objectives(Elsevier, 2024) Chen, Zhen-Song; Wang, Zhuo-Ran; Deveci, Muhammet; Ding, Weiping; Pedrycz, Witold; Skibniewski, Miroslaw J.The phase of operation and maintenance (O&M) is the most time-consuming and cost-intensive stage in the project life cycle. However, the potential benefits of Building Information Modeling (BIM) in this phase have not been fully explored, unlike in the design and construction phases. This is particularly evident in the absence of a comprehensive assessment of its application capabilities. In light of this setting, we develop a BIM maturity assessment model (BIM MAM) for the project's O&M phase. The proposed model comprises of an assessment indicator system that facilitates experts in providing individual assessment results, and a collective opinion aggregation method in a probabilistic context based on a multi-objective optimization model that is employed to generate the ultimate collective assessment results. The established multi-objective optimization model for BIM MAM incorporates the influence of human behavior factors on the final results by introducing the fairness concern utility level as an objective. Finally, we take Wuhan Jiangxia Sewage Treatment Plant as a practical case to illustrate the effectiveness and feasibility of the proposed BIM MAM.Öğe Optimized decision support for BIM maturity assessment(Elsevier, 2023) Chen, Zhen-Song; Zhou, Meng-Die; Chin, Kwai-Sang; Darko, Amos; Wang, Xian-Jia; Pedrycz, WitoldBuilding information modeling (BIM) maturity models occupy a crucial role in guiding BIM-reliant stakeholders and enterprises to identify BIM capabilities and facilitate process improvements. Nevertheless, few quantitative BIM maturity models are available for the measurement and improvement of BIM utilization performance. This study designs a refined assessment system for the maturity measurement of BIM-based projects during the design and construction stages. The advocated BIM maturity model combines a probability distribution function aggregation paradigm and a large-scale group decision-making framework to provide an expert-based assessment system for evaluating project-based BIM performance. The case study of the Corning Gen 10.5 glass substrate production line workshop in Wuhan demonstrates the feasibility and effectiveness of the proposed model. This paper establishes a generalizable structural framework that can potentially facilitate BIM maturity analysis in a portfolio of projects or the industry as a whole and will generate fresh insight into designing quantitative BIM maturity models across various contexts.Öğe Requirement-driven sustainable supplier selection: creating an integrated perspective with stakeholders' interests and the wisdom of expert crowds(Elsevier Ltd, 2023) Chang, Jian-Peng; Chen, Zhen-Song; Wang, Xian-Jia; Martínez, Luis; Pedrycz, Witold; Skibniewski, Miros?awDriven by the pressure from various stakeholders to embrace sustainability and from the market to enhance competitiveness, more and more companies have been committed to conducting sustainable supplier selection (SSS). SSS is a requirement-driven multi-criteria decision-making (MCDM) problem and the existing literatures have attempted to integrate quality function deployment (QFD) with various MCDM methods to model the problem. However, these models have not conducted SSS based on stakeholders’ satisfaction from alternatives, and used the weights of criteria obtained from stakeholders’ preferences for requirements, which is used to model the compensation among satisfaction levels of different requirements, to guide the compensation among alternatives’ performances on different criteria. In order to address these deficiencies, this paper develops a novel generalized (QFD)-based MCDM structure for SSS, based on which we further design a QFD-based multi-stakeholders and multi-experts MCDM model considering stakeholders’ consensus of interest and wisdom of expert crowds. Firstly, multiple stakeholder groups with multiple participants in each one are asked to output their expectations and preferences for their own requirements; within each group, the algorithm for consensus reaching process (CRP) is designed to formulate the acceptable collective expectations, and the best worst method (BWM) method integrated with an algorithm for consistency improving process (CIP) and an algorithm for CRP is developed to weight requirements. Secondly, multiple experts use basic uncertain linguistic information (BULI) to characterize performances of alternatives on criteria and relationship between requirements and criteria, and a method of weighting individual judgements with the capability of drawing on wisdom of crowds (WOC) while accommodating organizers’ trust level in reliability given by experts is used to facilitate formulating collective judgements. Thirdly, for each alternative and each requirement of each stakeholder group, we can quantify the comprehensive performance of the alternative on the requirement with the help of QFD, based on which and the group's expectation we can use the value function of prospect theory to quantify the group's satisfaction of the requirement from the alternative; aggregating the weighted satisfaction levels of requirements gives rise to the comprehensive satisfaction level of each group from each alternative; and then the Maximin decision rule is introduced to rank alternatives because of its ability of accommodating the non-compensation among different stakeholder groups. Finally, a case study is conducted to investigate the validity and effectiveness of the proposed model. © 2022 Elsevier LtdÖğe Safety Perception Evaluation of Civil Aviation Based on Weibo Posts in China: An Enhanced Large-Scale Group Decision-Making Framework(Springer Heidelberg, 2023) Feng, Si-Hai; Xin, Yao-Jiao; Xiong, Sheng-Hua; Chen, Zhen-Song; Deveci, Muhammet; Garcia-Zamora, Diego; Pedrycz, WitoldThe massive spread of COVID-19 and the crash of China Eastern Airlines MU5735 have negatively impacted the public's perception of civil aviation safety, which further affects the progress of the civil aviation industry and economic growth. The aim of research is to investigate the public's perception of China's civil aviation safety and give the authorities corresponding suggestions. First, we use online comment collection and sentiment analysis techniques to construct a novel evaluation index system reflecting the public's greatest concern for civil aviation safety. Then, we propose two novel large-scale group decision-making (LSGDM) models for aggregating evaluation: (1) K-means clustering with a novel distance measure for evaluators combined with unsupervised K-means clustering in two-stage, (2) unsupervised K-means clustering for evaluators combined with unsupervised K-means clustering for processing evaluation in two-stage. Finally, we compare the characteristics of different models and use the average of the two models as the final evaluation results.Öğe Two improved N-two-stage K-means clustering aggregation algorithmic paradigms for HFLTS possibility distributions(Elsevier, 2023) Xiong, Sheng-Hua; Xin, Yao-Jiao; Chen, Zhen-Song; Rodriguez, Rosa M.; Feng, Si-Hai; Martinez, Luis; Pedrycz, WitoldThe available method based on statistical principles for aggregating hesitant fuzzy linguistic term set (HFLTS) possibility distribution is the N-two-stage algorithmic aggregation paradigm driven by the K-means clustering (N2S-KMC). Nonetheless, the N2S-KMC method is subject to two significant limitations. (i) The grouping technique is capable of effectively partitioning decision-making information into N groups. However, it does not determine the appropriate placement of members within each group, as the number of computations is dependent on the number of elements present in each group, rather than the elements themselves. (ii) The initial clustering centers of K-means clustering are chosen without adhering to the distribution law within the aggregated hesitant 2-tuple linguistic terms set (H2TLTS) possibility distribution. This may result in a reduction in the clustering performance. In order to address the aforementioned limitations, we suggest two enhancement techniques for the former. Firstly, we propose the utilization of the minimum average difference (MAD) method to ascertain the number of groups. This approach aims to reduce the time required for the initial stage of aggregation following grouping. Secondly, we recommend the implementation of the maximize compactness degree of inter-group grouping (MCDIGG) method. This method enables the identification of group members, resulting in a more concentrated distribution of data subsequent to grouping. The present study suggests the utilization of MAD and MCDIGG techniques as a substitute for the grouping approach in the N2S-KMC model. This leads to the development of a new algorithm, IN2S-DO-KMC, wherein the data is partitioned into K subsets in a descending order to determine the initial center for KMC. Furthermore, with respect to the issue present in the subsequent phase, we propose the utilization of the density canopy (DC) algorithm to perform pre-clustering of the data and produce the initial clustering center and the quantity of clusters for the K- means algorithm. Subsequently, a refined version of the N2S-KMC model, denoted as IN2S-DC-KMC, has been suggested. Ultimately, an empirical study is conducted to assess the validity and practicability of the proposed framework for evaluating failure modes in medical devices. The outcomes are evaluated with regards to the efficacy of the algorithm, the numerical dispersion, and the pragmatic ramifications.Öğe Using I-subgroup-based weighted generalized interval t-norms for aggregating basic uncertain information(Elsevier, 2024) Yang, Yi; Chen, Zhen-Song; Pedrycz, Witold; Gomez, Marisol; Bustince, HumbertoIn this paper, we present a method of extending t-norms and t-conorms to a given closed subinterval [a, b] in [-infinity,+infinity], which preserves the basic properties of these fuzzy connectives and their one-to-one correspondence with operations in a I-semigroup. Subsequently, the generalized De Morgan triples, weighted generalized interval t-norms and t-conorms are provided to construct the aggregation function for extended basic uncertain information. Eventually, we apply the proposed aggregation function to establish the basic uncertain linguistic information-based aggregation operator.Öğe Vulnerability analysis of China's air and high-speed rail composite express network under different node attack strategies(Springer, 2023) Mu, Nengye; Xin, Peiyuan; Wang, Yuanshun; Cheng, Chiyao; Pedrycz, Witold; Chen, Zhen-SongThe current study centers on the vulnerability of the express network. It involves the development of a composite express network in China that integrates air and high-speed rail transportation, utilizing real-world data. The network's vulnerability is evaluated through simulation analysis. This study develops a model for calculating the value of node states that takes into account both the topological position and practical utility of said nodes. A node importance calculation model is developed by utilizing the information entropy theory and the pattern of cluster distribution. Two distinct strategies for node attacks have been developed: single-point failure and multiple-point failure. A system for assessing network vulnerabilities has been developed, which utilizes alterations in network structure and functional impairments to simulate and evaluate the vulnerability of the air and high-speed rail composite express network. The findings suggest that nodes exhibiting singular transportation modes and limited external connectivity are more vulnerable to cascading effects. Frequently, these nodal points are affiliated with provinces or self-governing territories that are situated in geographically remote areas and exhibit comparatively lower degrees of economic advancement. Improving the development of urban air and high-speed rail infrastructure, as well as augmenting the connectivity of air and high-speed rail express services, are essential measures to strengthen the self-risk resistance capacity of these hubs. Optimizing the network structure and modifying the internal topological and transportation coupling relationships can enhance the overall performance of the network, thereby bolstering the risk resilience of the air and high-speed rail composite express network holistically. The aforementioned discoveries offer novel perspectives for aviation enterprises and railway departments in their decision-making regarding air-rail intermodal strategies, as well as for the development of all-encompassing transportation network planning.
