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This paper investigates decision making with normalized probabilistic linguistic preference relations (NPLPRs). Consistency analysis is indispensable for deriving the reasonable ranking. After recalling previous research, we find that all previous concepts cannot fully define consistent NPLPRs. As a fundamental topic of decision making with preference relations, it is necessary to further study the consistency of NPLPRs. For this purpose, an interactive algorithm for deriving disjunctive probabilistic additive linguistic preference relations (DP-ALPRs) is provided, by which an additive consistency concept for NPLPRs is defined. When NPLPRs are unacceptably consistent, models for obtaining acceptably additively consistent NPLPRs are built. Considering the situation where only incomplete NPLPRs are obtained, a disjunctive probability and additive consistency based interactive algorithm for ascertaining missing judgments is provided. Meanwhile, we discuss group decision making (GDM) with NPLPRs and offer a distance measure based formula to determine the weights of the decision makers. In addition, the method defines a consensus index and builds models for improving the consensus level. Under the additive consistency and consensus discussions, an interactive algorithm for GDM with NPLPRs is proposed. Finally, the new method is applied to select green raw material suppliers to illustrate the application and compared with several previous ones. [ABSTRACT FROM AUTHOR]

Information quality has attracted increasing attention in recent years. In this paper, we propose a measure to quantify the information quality of intuitionistic fuzzy information based on a pseudo probability transformation, and derive its induced order to rank intuitionistic fuzzy values. The proposed measure and the induced order are proved to possess some well-defined properties that ensure rationality. Further, in order to rank intuitionistic fuzzy alternatives in decision making, we turn the proposed information quality into a ranking-oriented information quality, and present its induced order to generate the ranking. The decision capacity of our method is founded with the goal of rationalizing, simulating and further facilitating the interpretability and transparency in human decision making. A comparative study has been conducted, demonstrating the effectiveness of the proposed approach to ranking alternatives. The proposed approach is shown to outperform existing ranking methods via case-by-case comparisons. Finally, an application involving decision making of drug selection and an application in multi-criteria decision analysis of supplier selection are provided. [ABSTRACT FROM AUTHOR]

Increases in population and prosperity are linked to a worldwide rise in garbage. The "classification" and "recycling" of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model. • Develop a 2-layer decision system for waste sorting using deep learning. • Construct MFDM using fusion scores, criteria, and deep waste sorting models. • Entropy and VIKOR methods rank criteria and deep learning models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA), as a powerful and effective risk assessment tool in reliability and safety analysis, has been extensively applied in different fields to enhance the reliability of a system. However, the traditional FMEA method has exposed some significant defects in practical applications. Furthermore, the risk ranking of failure modes is not convenient for subsequent maintenance strategies, and the interactions between risk criteria are not considered in most multiple criteria decision making (MCDM)-based methods. In this paper, we construct a novel FMEA model based on criteria importance through the inter-criteria correlation (CRITIC) method and three-way decisions to improve the performance of the conventional FMEA method and aid making a maintenance plan. Firstly, we introduce the decision-theoretic rough sets (DTRSs) into an interval 2-tuple linguistic (I2TL) environment to define interval 2-tuple linguistic decision-theoretic rough sets (I2TLDTRSs). Secondly, the CRITIC method is employed to calculate the weight of risk criteria. Thirdly, the conditional probability is obtained by a similarity-based method with the help of the ideas of the TOPSIS approach, and a relative loss function is designed by introducing risk avoidance coefficient. Fourthly, the classification of failure modes is derived according to the three-way decision rules. Finally, two numerical examples of the train door system are given to verify the effectiveness and practicality of the presented model. The presented model both extends the theory and application of three-way decisions and provides a solution beneficial for making a maintenance strategy to reduce the risk of failure modes. [ABSTRACT FROM AUTHOR]