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    Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making
    (Pergamon-Elsevier Science Ltd, 2024) Deveci, Muhammet; Pamucar, Dragan; Gokasar, Ilgin; Martinez, Luis; Koppen, Mario; Pedrycz, Witold
    Metaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker.
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    Assessing alternatives of including social robots in urban transport using fuzzy trigonometric operators based decision-making model
    (Elsevier Science Inc, 2023) Deveci, Muhammet; Pamucar, Dragan; Gokasar, Ilgin; Zaidan, Bilal Bahaa; Martinez, Luis; Pedrycz, Witold
    Current trends point to a not-too-distant future with qualitatively advanced interactions between humans and social robots. It is critical to consider the possibility of forming meaningful social relationships with robots when defining the future of human-robot interactions, as well as studying how these interactions will evolve to the point where humans are unable to distinguish between humans and robots in urban transportation. In this study, the advantages of using social robots in urban transportation are prioritized by using a multi-criteria decisionmaking tool, which consists of two consecutive stages, namely: i) a novel fuzzy sine trigonometry based on the logarithmic method of additive weights (fuzzy ST-LMAW) that is proposed to calculate the criteria weights; ii) a nonlinear fuzzy Aczel-Alsina function based the weighted aggregate sum product assessment (fuzzy ALWASWASPAS) that is developed to select and rank the alternatives. The proposed model enables flexible nonlinear processing of complex and uncertain information encountered in real applications. A case study is developed to rank three alternatives with twelve sub-criteria grouped into four aspects using the proposed method. The results show that the most advantageous alternative is to replace people with social robots as safety drivers in level four autonomous vehicles due to their possible impact on transportation.
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    Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy
    (Pergamon-Elsevier Science Ltd, 2024) Alsattar, Hassan A.; Qahtan, Sarah; Zaidan, Aws Alaa; Deveci, Muhammet; Martinez, Luis; Pamucar, Dragan; Pedrycz, Witold
    This study presents a novel dynamic localisation-based decision (DLBD) with fuzzy weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic hesitant fuzzy set (PSVNHFS) environment to benchmark Hybrid Multi Deep Transfer and Machine Learning (HMDTML) models. The novel DLBD method is proposed to generate a dynamic localisation decision matrix based on the upper and lower boundaries and the length of the scale. The superiority of DLBD derives from its ability to manage dynamic changes with boundary value consequences. In addition, the utilization of PSVNHFS in conjunction with DLBD and FWZIC has proven to effectively address the challenges posed by vagueness, uncertainty and hesitancy in the benchmarking procedure. The proposed methodology consists of three primary three steps: i) the adaptation of 48 HMDTML models, including 4 deep transfer learning models and 12 machine learning models trained on a dataset of 936 chest Xray images obtained from both COVID-19 patients and individuals without the disease. Then, these models were evaluated based on seven evaluation criteria, and a decision matrix was proposed. ii) The development of a PSVNH-FWZIC to assign weights to the evaluation criteria. iii) The formulation of a PSVNH-DLBD for the purpose of benchmarking HMDTML models. Results of the PSVNH-FWZIC revealed that AUC and time were the most important evaluation criteria, while precision was the least important. Furthermore, the results from PSVNH-DLBD, reveal that Model M24 (Painters-Decision Tree) earned the highest rank when & lambda; = 2,3,4, 5and6, followed by Model M25 (SqueezeNet-AdaBoost) and Model M34 (DeepLoc-kNN), while Model M39 (DeepLocSVM) had the lowest rank (rank = 48) across all & lambda; values. The proposed method underwent sensitivity and comparison analyses to confirm its reliability and robustness.
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    Minimum cost consensus model with altruistic preference
    (Pergamon-Elsevier Science Ltd, 2023) Liang, Yingying; Ju, Yanbing; Tu, Yan; Pedrycz, Witold; Martinez, Luis
    The minimum cost consensus model (MCCM) aims at reaching group consensus for either conflict or polarized opinions evaluated in group decision making. Decision makers (DMs) consider both their own and other interests in real-world minimum cost consensus problems, exhibiting the altruistic preference behavior. To quantify the behavior, we define a satisfaction degree function to reflect the interaction among DMs. On this basis, the MCCM with altruistic preference (MCCM-AP) is built, in which a novel consensus measurement for concentrated or scattered opinions is introduced. Furthermore, the MCCM-AP based satisfaction improvement model is established. Finally, an illustrative example and a practical case study are carried out to illustrate the performance of the proposed models, together with sensitivity and comparative analyses are conducted to explore the impact of altruistic preference and discuss the merits of our proposal. The findings indicate that total adjustment cost is nonincreasing with the increase of altruistic preference degree, which provides the decision support for managers to handle the altruistic preference behavior and reduce the consensus cost.
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    Requirement-driven supplier selection: a multi-criteria QFD-based approach under epistemic and stochastic uncertainties
    (Springer, 2024) Chang, Jian-Peng; Ren, Heng-Xin; Martinez, Luis; Pedrycz, Witold; Chen, Zhen-Song
    Supplier selection (SS) has emerged as a critical challenge for companies aiming to enhance the operational management of their supply chains, a task that has grown in complexity with the advent of Industry 4.0 and the ongoing digital transformation. Recognizing the gaps in current literature-specifically, the lack of consideration for stakeholders' expectations in guiding SS, as well as the inadequate handling of epistemic and stochastic uncertainties-this paper introduces a multiple-criteria Quality Function Deployment (QFD)-based model for SS. To address epistemic uncertainty, we put forward a novel subjective judgment representation method, which is named as linguistic term set integrated with discrete subjective probability distribution (LTS-DSPD), to enable decision-makers to express their judgments in a manner that is both simpler and more nuanced. Furthermore, we also give the elicitation methods and computing techniques for LTS-DSPD. Then, we integrate stakeholders' requirements, along with their preferences and expectations for these requirements to inform and guide SS. To effectively operationalize this guidance, we design the QFD-based methods to transform stakeholders' inputs into the assessment criteria for SS, the weights of criteria, and the expectations for the performances of suppliers on each criterion, respectively. To address stochastic uncertainty, we have developed an innovative methodology for characterizing it, and adopt prospect theory to quantify the overall utility of alternative suppliers. The paper concludes with a case study to demonstrate its practical application and effectiveness in streamlining SS process.
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    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, Witold
    The 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.