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Risk management plays a vital role in ensuring the safety and efficiency of tunnel construction by considering various factors, including uncertainties associated with concurrent adverse sources. One key aspect of risk management is prioritizing hazardous zones to devise an optimal countermeasure plan within time and cost constraints. This study developed an advanced tunnel risk management model, combining the analytic hierarchy process (AHP) and fuzzy set theory (FST). The model derived the impact using AHP and the probability using FST. By selectively combining causal factors that met the selection criterion, the risk of each hazardous zone was determined, enabling the prioritization of identified hazardous zones. The model application results indicated that causal combinations associated with significant tunnel convergence posed a relatively high risk. Moreover, the hazardous zones where unstable ground formations were excavated by a gripper tunnel boring machine (TBM) were revealed as the most vulnerable locations. Consequently, adopting a shield TBM or implementing ground reinforcement is recommended. Overall, the developed model effectively prioritizes identified hazardous zones and provides an optimal countermeasure plan, contributing to the overall safety and efficiency of the operations.

Control charts (CCs) are very effective tools to follow process variation and to improve process quality. It is completely critical to increase the sensitiveness and flexibility of CCs to gain a deeper perspective for process control. When constructing conventional CCs, errors may occur due to operator or measuring instruments in performing the measurement. By the way, some data related to CCs can include “uncertainty” or “vagueness” related to process and human evaluations or inspector judgments. We know that classical CCs cannot be able to manage this process. The fuzzy set theory (FST) is one of the most important tools to solve these problems, and the CCs designed based on FST are more usable and preferable for monitoring the process. By the way, one of the extensions of FST named type-2 fuzzy sets that have fuzzy membership degrees is more capable of modeling uncertainty. Therefore, they can be successfully used to the design of the control process in a more flexible and sensitive way. In this paper, the type-2 fuzzy sets have been used to the design of variable control charts to increase the precision and flexibility of them. For this aim, x ¯ - R , x ¯ - S and I - M R control charts have been re-designed by using type-2 fuzzy sets. Additionally, these charts have been applied on a real case application from electronic industry, and the obtained results indicate that CCs based on type-2 fuzzy sets can evaluate the process in a more sensitive and flexible way.

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

The recognition of membership function by knowledge acquisition from experts is an important factor for many fuzzy mathematical programming models. Meanwhile, hesitant fuzzy set theory as a known and popular modern fuzzy set by assigning some discrete membership degrees under a set could appropriately deal with imprecise information in decision-making problems. Thus, the Hesitant Fuzzy Membership Function (HFMF) estimation could help users of the mathematical programming approaches to provide a powerful solution in continuous space problems. Therefore, this study proposes a possibilistic programming approach based on Bezier curve mechanism for estimating the HFMF. In the process of possibilistic programming approach, an optimization model is presented to tune the primary parameters of Bezier curve by the goal of minimizing the SSE) between the empirical data and fitted HFMF. After that, the efficiency and applicability of the proposed approach is checked by proposing a novel mathematical model for biomass supply chain network design problem. Finally, a computational experiment and validation procedure about the biomass supply chain network design is provided to peruse the verification and validation of the proposed approaches.

Publications on fuzzy set theory and its applications have grown exponentially. The increasing rate of developments in the field is a response to diverse factors, including the need for robust mathematical approaches that model human-like perceptions, values and decision-making processes in complex and dynamic systems. This study presents a citation analysis of 22 narrowly targeted fuzzy set theory journals with a focus on leading authors and research areas. In this paper, bibliometric tools are used for the treatment and analysis of a large amount of data retrieved from the rigorous Web of Science scientific database. The aim of the paper is to offer a general overview of the influence that fuzzy set theory has on academicians and diverse scientific fields. Its objective is to identify connections, trends and opportunities for synergies. The results of over 62,000 published documents, which represent more than 1,300,000 citations in the selected journals, show computer science and engineering as the top citing research fields and authors Xu, Pedrycz and Herrera as the top citing researchers.

In today’s big-data era, enterprises are able to generate complex and non-structured information that could cause considerable challenges for CPA firms in data analysis and to issue improper audited reports within the required period. Artificial intelligence (AI)-enabled auditing technology not only facilitates accurate and comprehensive auditing for CPA firms, but is also a major breakthrough in auditing’s new environment. Applications of an AI-enabled auditing technique in external auditing can add to auditing efficiency, increase financial reporting accountability, ensure audit quality, and assist decision-makers in making reliable decisions. Strategies related to the adoption of an AI-enabled auditing technique by CPA firms cover the classical multiple criteria decision-making (MCDM) task (i.e., several perspectives/criteria must be considered). To address this critical task, the present study proposes a fusion multiple rule-based decision making (MRDM) model that integrates rule-based technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) into MCDM techniques that can assist decision makers in selecting the best methods necessary to achieve the aspired goals of audit success. We also consider potential implications for articulating suitable strategies that can improve the adoption of AI-enabled auditing techniques and that target continuous improvement and sustainable development. First published online 7 September 2020

We investigate an optimization problem of robust control for the Delta-type parallel manipulator. The task is to render the uncertain Delta-type parallel manipulator to follow the pre-specified active constraints. Uncertainties in this paper are deemed to be (possibly fast) time-varying and bounded, and the information of boundaries is creatively depicted via a fuzzy set. By this fuzzy depiction, a robust control scheme, which is deterministic and not the traditional if-then rules-based, is designed. On top of that, we construct an optimization problem that targets the choice of the performance-dependent control parameter. Then, the existence and uniqueness of the global solution to this problem, which could be solved by minimizing a fuzzy performance index, is proven. In addition to meeting active constraints, the Delta-type parallel manipulator under the proposed control scheme assures two attractive performances: the deterministic performance and the fuzzy performance. The results of simulations illustrate the validity and practicability of the control scheme.

In this study, a novel multiple-pollutant waste load allocation (WLA) model for a river system is presented based on the National Sanitation Foundation Water Quality Index (NSFWQI). This study aims to determine the value of the quality index as the objective function integrated into the fuzzy set theory so that it could decrease the uncertainties associated with water quality goals as well as specify the river's water quality status rapidly. The simulation-optimization (S-O) approach is used for solving the proposed model. The QUAL2K model is used for simulating water quality in diferent parts of the river system and ant colony optimization (ACO) algorithm is applied as an optimizer of the model. The model performance was examined on a hypothetical river system with a length of 30 km and 17 checkpoints. The results show that for a given number of both the simulator model runs and the artificial ants, the maximum objective function will be obtained when the regulatory parameter of the ACO algorithm (i.e., q0) is considered equal to 0.6 and 0.7 (instead of 0.8 and 0.9). Also, the results do not depend on the exponent of the membership function (i.e., γ). Furthermore, the proposed methodology can find optimum solutions in a shorter time.

As China's power market becomes more orderly, electricity retailing companies are influenced by multiple factors restricting their healthy, stable, and sustainable development. This paper explores these issues through four dimensions, the external basic environment, operating credit risk, financial credit risk, and transaction credit risk, and determines the credit risk evaluation index system, which includes 22 factors relevant to electricity retailing companies. To achieve this, this paper utilizes the characteristics of the intuitionistic fuzzy set theory and, in an uncertain environment, the proposed credit risk assessment model based on the intuitionistic fuzzy analytic hierarchy process (IFAHP). In order to improve the identification degree of the credit risk evaluation of electricity retailing companies, a penalty factor is introduced into the model, and the variable weight mechanism of dynamic adjustment hesitancy is proposed. Finally, we select five electricity retailing companies and use different evaluation methods to conduct a comprehensive credit risk evaluation, so as to improve the credit risk level of electricity retailing companies. This paper utilizes the characteristics of the intuitionistic fuzzy set theory and, in an uncertain environment, the proposed credit risk assessment model based on the intuitionistic fuzzy analytic hierarchy process (IFAHP), and selects five electricity retailing companies and uses different evaluation methods to conduct a comprehensive credit risk evaluation, so as to improve the credit risk level of electricity retailing companies.

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.