Fuzzy Operators Research Articles

Leakage failure probability assessment of submarine pipelines using a novel pythagorean fuzzy bayesian network methodology

Obtaining precise and adequate failure data can be challenging in the probabilistic risk assessment of process industries, like submarine pipelines. This study proposes a novel approach, the Pythagorean fuzzy Bayesian network methodology, to tackle the challenge of obtaining prior data in the assessment of leakage risk in submarine pipelines. First, the qualitative evaluation of experts is converted by Pythagorean fuzzy sets. Next, the enhanced Pythagorean trapezoidal fuzzy Einstein hybrid geometric operator is integrated with the subject-objective weighting approach to consolidate the expert opinions in order to obtain prior probabilities. Following this, the leaky Noisy-OR gate in the Bayesian network is utilized to access the conditional probabilities, which depict the uncertain logical connection of events. Ultimately, the Bayesian network is utilized for inference and analysis to anticipate the probability of system failure and detect any vulnerabilities. Furthermore, a case study is performed to demonstrate the practicality of the approach. The reliability of the methodology is verified by results comparison and analysis. The suggested approach and evaluation findings can offer valuable guidance for the safety supervision of the submarine pipeline network.

A sustainability evaluation framework for the urban energy Internet using the Fermatean fuzzy Aczel-Alsina hybrid MCDM method

The evaluation of urban energy internet (UEI) is crucial for the establishment of low-carbon cities. This paper aims to present a hybrid decision-making framework to address UEI evaluation problems from a sustainability perspective. First, the Fermatean fuzzy Aczel-Alsina power aggregation operators are proposed to aggregate evaluation information, and the remarkable properties of these operators are explored. Then, the combined weight of the criteria is determined through the decision-making trial and evaluation laboratory method and the Shannon entropy-based power weighted method within the Fermatean fuzzy circumstance. Moreover, the classic multiobjective optimization by ratio analysis plus the full multiplicative form method is extended using the proposed aggregation operators. Finally, an index system was constructed from a sustainability perspective, and the level of development of the four UEI projects is assessed using the proposed method. The experimental results demonstrate that the proposed method can provide beneficial guidelines for the construction and development of the UEI. Furthermore, the reliability and effectiveness of the proposed decision-making framework are validated through sensitivity analysis and a comparative study with existing MCDM methods.

ωA-IvE methodology: Admissible interleaving entropy methods applied to video streaming traffic classification

This work focuses on the width-based interval fuzzy entropy notion, considering the interval data diameter as a measure of the lack of knowledge and uncertainty related to the precise membership degrees of elements in an interval-valued fuzzy set. The w-preserving notion relates the uncertainty from input to output in system information. Such a concept generates a new entropy by applying width-based average functions and admissible order to compare interval data and define width-based fuzzy connectives in data fuzzy computations. A new admissible total order is introduced, requesting just one injective and increasing function, illustrated by a Decimal Digit Interleaving (DDI) function. The proposal methods are based on the admissible interleaving order and related expressions for width-based interval entropy considering different conditions for composition among width-based interval fuzzy operators, as negations with equilibrium and average functions, also including idempotent aggregation function and restricted equivalence functions. Finally, we illustrate the application of the proposed methods for solving a video streaming traffic classification problem. The admissible interleaving entropy analysis is performed over the attributes modeling the FuzzyNetClass approach, a computational model for traffic classification related to video streaming, exploring the integration of inference systems based on interval-valued fuzzy logic and machine learning algorithms. The results by comparison with other width-based interval fuzzy entropy methods were promising and pointed to continuing study and research efforts.

Retracted: Novel Analysis of Fuzzy Physical Models by Generalized Fractional Fuzzy Operators

Retracted: Novel Analysis of Fuzzy Physical Models by Generalized Fractional Fuzzy Operators

Fuzzy cross-efficiency evaluation based on prospect theory and regret theory

Cross-efficiency in data envelopment analysis is widely used in production as an evaluation method that includes input and output indicators and allows for self-evaluation and mutual evaluation of decision making units (DMUs). However, as the application scenarios continue to expand, the traditional methods gradually fail to meet the needs. Many researchers have proposed improved methods and made great progress in weight determination, but the existing studies still have shortcomings in considering the psychological behavior of decision makers (DMs) and there is still relatively little research on cross-efficiency in fuzzy environments. In this paper, we proposed a method to apply CRITIC to determine weights and introduce both prospect theory and regret theory into the evaluation method of cross-efficiency to obtain the prospect cross-efficiency matrix and regret cross-efficiency matrix respectively, and then applied the Pythagorean hesitant fuzzy operator to aggregate them to achieve the ranking of DMUs through the fraction function. This largely takes into account the subjective preference and regret avoidance psychology of DMs. The applicability of this paper’s method is also verified through an example of shopping for a new energy vehicle. Finally, the effectiveness of this paper’s method is verified by comparing three traditional methods with this paper’s method, which provides an effective method for considering risk preferences in the decision-making process.

Multi-attribute fuzzy pattern decision making based on information systems

This paper introduces an innovative approach aimed at enhancing multi-attribute decision-making through the utilization of fuzzy pattern recognition, with a specific emphasis on engaging decision-makers more effectively. The methodology establishes a multi-attribute fuzzy pattern recognition model within a hybrid information system framework. It categorizes attributes into natural and abstract groups, standardizes them, and employs membership functions to transform them into degrees of membership. This adaptable approach permits the derivation of various decision criteria from the hybrid system. Subsequently, a testing set is generated from this system, and a suitable fuzzy operator is selected. The optimal solution is determined by assessing the similarity between the standard and testing sets. To underscore its effectiveness, a practical example is provided. Crucially, in the realm of multi-attribute decision-making, our method simplifies the process by reducing computational steps in contrast to the conventional TOPSIS model, while maintaining consistent outcomes. This streamlines the decision-making process and reduces complexity. We also demonstrate its applicability in multi-objective decision-making through a case study evaluating exemplary educators, thereby highlighting its adaptability and effectiveness. This method exhibits significant promise for enhancing multi-attribute decision-making and offers practical applications.

Research on a fuzzy algorithm for intelligent power-saving control of urban street lamps

Urbanisation has increased the demand for energy-efficient and environmentally friendly street lighting systems. However, traditional urban lighting uses time interval control and cannot be intelligently controlled according to the environment. Therefore, an intelligent urban street lighting control system based on advanced fuzzy control technology was developed. The system uses a fuzzy control algorithm based on ZigBee technology to study the street lighting system. The fuzzy control system determines input and output variables and obtains demand instructions for intelligent control of street lights through fuzzy operation. The number of node visits and node degree were optimised to complete the construction of the street lamp intelligent control system. Experimental simulations showed that the proposed scheme could reasonably control the brightness of the street lighting according to ambient light. At 5.00 p.m., the proposed scheme adjusts the street lighting to 80 W. The system is illuminated at the rated power, and the current power consumption is 100 W. A test was carried out under a street lighting system, and the proposed scheme dynamically adjusted the lighting brightness reasonably considering various factors such as light intensity, noise intensity and vehicle speed. The proposed street lamp fuzzy control algorithm has good control performance and significantly reduces the system energy consumption. The research has important reference value for energy saving and intelligent control of urban lighting.

Four new intuitionistic fuzzy bimodal topological structures

Two intuitionistic fuzzy modal operators from a second type are used for introducing of new intuitionistic fuzzy modal topological structures. After this, these structures, extended with the two standard intuitionistic fuzzy modal operators, generate four new intuitionistic fuzzy bimodal topological structures. They are illustrated with examples.

Algorithms for decision-making process using complex Pythagorean fuzzy set and its application to hospital siting for COVID-19 patients

Since the start of COVID-19, a fair amount of work has been undertaken by scholars around the world to model its progression. It became clear from the start of pandemic that its progression is affected by various factors within different communities. Subsequently, the necessary means and the range of measures used to effectively control the virus would vary from place to place. And we have been witness to different approaches adopted around the world to maintain the virus under check both in the short term and the long term. So, in this unexpected situation, it is a great challenge for the world health organization (WHO) to save the lives of COVID-19 patients. For this, several mathematical models have been made for better understanding the coronavirus contagion. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. Thus, in this unexpected situation, fuzzy sets (FSs) are considered due to their inherent capability to deal with uncertainty. Fuzzy sets (FSs) theory has the ability to manage uncertain situations. Thus, the goal of this research is to present newly mathematical methods based on complex Pythagorean fuzzy sets (CPyFSs) and their operators, namely complex Pythagorean fuzzy Einstein weighted geometric operator, and induced complex Pythagorean fuzzy Einstein hybrid geometric operator to reduce the spreading rate of COVID-19. At the end of the paper an illustrative example is constructed to show the effectiveness, reliability of the new techniques.

Some new results on intuitionistic fuzzy operators

The concept of intuitionistic fuzzy sets is becoming very popular day by day among mathematicians and researchers from other areas of science. It has been noticed that some operators have very interesting properties in Intuitionistic fuzzy sets. That is why these properties are investigated in this study. New findings on equality are obtained and proved.

Selection of best industrial waste management technique under complex non-linear Diophantine fuzzy Dombi aggregation operators

The harmful waste products, beverages, chemical agents that are discharged or emitted by industrial processes are categorized as industrial waste (IW). IW is one of the most severe problems facing governments around the world due to its environmental effect and severity. So the aim of this paper is to develop a method for selecting industrial waste management technique (IWMT) for industry. Dombi Norms were commonly referred to as Dombi operations, but their applications might be better expressed if they are presented with a new level of flexibility within the general parameter. Dombi operations have not yet been applied in non-linear Diophantine fuzzy sets (N-LDFSs) in a suitable form. Therefore we apply the Dombi operators on complex N-LDFS (CN-LDFS) and develop a generalized area of research in decision making problems (DMPs). The majority of decision-making challenges in the actual world contain a wide range of elements that must be taken into account. Making decision under such circumstances can frequently be challenging. To deal with such complicated issues, we require multi-attribute group decision-making (MAGDM) techniques. The aims to discuss MAGDM issues, this paper will introduce a new COmbinative Distance-based ASsessment (CODAS) methodology. In this paper, we put forward three novel aggregation operators (AOs): complex non-linear Diophantine fuzzy Dombi weighted averaging (CN-LDFDWA), complex non-linear Diophantine fuzzy Dombi order weighted averaging (CN-LDFDOWA), and complex non-linear Diophantine fuzzy Dombi hybrid weighted averaging (CN-LDFDHWA). These operators are used in the operational parameter to help a successful solution of the problem. Due to the growing energy and material shortages, as well as to comply with environmental laws and regulations and to take advantage of disposal economics in the current context, industrial waste (IW) should be taken into consideration as one of the potential resources in the steel industry. Further, the proposed decision support model applied to solve a case study regarding to IWMT for Pakistan steel mill corporation (PSMC) under complex N-LDF Dombi operators. The proposed method is more effective and reliable for decision making (DM) model, as compared to the existing method.

A Comparative Study of Fuzzy Domination and Fuzzy Coloring in an Optimal Approach

An optimal network refers to a computer or communication network designed, configured, and managed to maximize efficiency, performance, and effectiveness while minimizing cost and resource utilization. In a network design and management context, optimal typically implies achieving the best possible outcomes between various factors. This research investigated the use of fuzzy graph edge coloring for various fuzzy graph operations, and it focused on the efficacy and efficiency of the fuzzy network product using the minimal spanning tree and the chromatic index of the fuzzy network product. As a network made of nodes and vertices, measurement with vertices is a parameter for domination, and edge measurement is a parameter for edge coloring, so we used these two parameters in the algorithm. This paper aims to identify an optimal network that can be established using product outcomes. This study shows a way to find an optimal fuzzy network based on comparative optimal parameter domination and edge coloring, which can be elaborated with applications. An algorithm was generated using an optimal approach, which was subsequently implemented in the form of applications.

On n-polygonal interval-valued fuzzy numbers and application in e-commerce risk assessment

The polygonal interval-valued fuzzy number is constructed based on the polygonal fuzzy number and the interval-valued fuzzy number. Its main feature is that the linear operation of finite ordered points reduces the complexity of traditional interval-valued fuzzy number operations. This research presents a generalized distance formula between two polygonal interval-valued fuzzy numbers and explores topological properties under the distance of polygonal interval-valued fuzzy numbers. In addition, we adopt the TOPSIS (technique for order preference by similarity to an ideal solution) and prospect theory approach for the multi-attribute decision-making problem. The information of attributes describes with polygonal interval-valued fuzzy numbers, and we then implement optimized ranking on the alternatives according to the profit and loss ratio. Finally, we verify the effectiveness and practicability of the decision-making method and fuzzy numbers at polygonal interval-valued fuzzy numbers in e-commerce risk assessment.

Hamy mean operator under complex picture fuzzy environment and its application to disaster management program

Disaster Management Program plays a critical role in coordinating and implementing strategies to address emergencies and disasters, ranging from natural events like hurricanes, earthquakes, and wildfire to human-made incidents such as industrial accidents or terrorist attacks. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. This paper investigates the concept of complex picture fuzzy sets (CPFS), an extension of picture fuzzy sets (PFS), achieved by the inclusion of a phase term. The existence of phase terms expand the scope of CPFS from real line to a complex plane of unit disc and highlight its originality by demonstrating its capacity to handle both vagueness and periodicity simultaneously. In this paper, the complex picture fuzzy Hamy mean operator (CPFHM) and complex picture fuzzy dual Hamy mean operator (CPFDHM) is studied. The reason of selecting complex picture fuzzy Hamy mean operator (CPFHMO) is that it can find interrelationship among multi-input variables. Then the various properties of CPFHM and CPFDHM operator are described in depth. A multi-attributes group decision-making (MAGDM) technique for solving group decision-making problems is proposed based on these operators. The validity of the present technique is demonstrated by analyzing a disaster management problem. Furthermore we check the sensitivity of parameter k and apply the validity test on our proposed technique. Finally, a comprehensive comparison is provided between the proposed model and specific existing approaches, illustrating that the suggested decision model is superior and more advantageous than the existing employed methodologies.

Developing a Fuzzy Expert System for Diagnosing Chemical Deterioration in Reinforced Concrete Structures

The widespread application of reinforced concrete structures in different environmental conditions has underscored the need for effective maintenance and repair strategies. These structures offer numerous advantages, but are not impervious to the deleterious effects of chemical deterioration. The outcomes of this research hold significant implications for the management system of reinforced concrete structures. This study proposes the utilization of a fuzzy expert system as a means of enhancing the diagnosis of chemical deterioration in reinforced concrete structures that is a valuable tool for engineers and decision-makers involved in the maintenance and repair of these structures. The fuzzy expert system serves as an intelligent tool that can incorporate various symptoms of deterioration and inspection data to improve the accuracy and reliability of the diagnostic process. By integrating these inputs, the system evaluates 21 different data points, each representing a specific aspect of deterioration, on a scale ranging from 0 to 100. This numerical representation allows for a quantification of the level of deterioration, with 0 denoting minimal deterioration and 100 indicating severe deterioration. The effectiveness of the fuzzy expert system lies in its ability to process the vast amount of data and apply fuzzy operations on 352 fuzzy rules. These rules define the relationships between the inspection data, the type of deterioration, and its extent. Through this computational process, the fuzzy expert system can provide valuable insights into 10 distinct types of chemical deterioration, facilitating a more precise and comprehensive diagnosis. The implementation of the fuzzy expert system has the potential to revolutionize the field of diagnosing chemical deterioration in reinforced concrete structures. By addressing the limitations of traditional methods, this advanced approach can significantly improve the clarity and accuracy of the diagnostic process. The ability to obtain more precise information regarding the type and extent of deterioration is vital for developing effective maintenance and repair strategies. Ultimately, the fuzzy expert system holds great promise in enhancing the overall durability and performance of reinforced concrete structures in various environments.

Fast Dust Sand Image Enhancement Based on Color Correction and New Fuzzy Intensification Operators

Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and New Fuzzy Intensification Operators to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a fuzzy intensification operator to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images.

Fuzzy rough sets based on fuzzy quantification

Classical (fuzzy) rough sets exhibit sensitivity to noise, which is particularly undesirable for machine learning applications. One approach to solve this issue is by making use of fuzzy quantifiers, as done by the vaguely quantified fuzzy rough set (VQFRS) model. While this idea is intuitive, the VQFRS model suffers from both theoretical flaws as well as from suboptimal performance in applications. In this paper, we improve on VQFRS by introducing fuzzy quantifier-based fuzzy rough sets (FQFRS), which proposes an intuitive fuzzy rough approximation operator that utilizes general unary and binary quantification models. We show how several existing models fit inside FQFRS, as well as how it inspires novel ones. Additionally, we propose several binary quantification models to be used with FQFRS. Furthermore, we conduct a theoretical study of their properties, and investigate their potential by applying them to classification problems. In particular, we highlight the effectiveness of Yager's Weighted Implication-based (YWI) binary quantification model, which induces a fuzzy rough set model that is both a significant improvement on VQFRS, as well as a worthy competitor to the popular ordered weighted averaging based fuzzy rough set (OWAFRS) model.

Evaluation of sustainable strategies for urban parcel delivery: Linguistic q-rung orthopair fuzzy Choquet integral approach

For the first time, the critical problem of selecting the most sustainable strategy for urban parcel delivery is addressed and solved in this paper. It aims to help not only postal and logistics companies but also regulatory agencies and city authorities find an environmentally friendly, socially responsible, and economically viable solution. Three alternatives for organizing urban parcel delivery are defined, which are traditional, united consolidation center, and inner-city hubs. According to the three pillars of sustainability, an assessment framework for practitioners is proposed. This paper introduces a linguistic q-rung orthopair fuzzy Choquet integral approach to evaluate sustainable strategies for urban parcel delivery. The linguistic q-rung orthopair fuzzy Choquet integral averaging and linguistic q-rung orthopair fuzzy Choquet integral geometric operators have been constructed for the first time for this purpose. The operator-related qualities are demonstrated. The operators are used to create a multi-criteria group decision-making approach. The case study of the city of Belgrade illustrates the practical applicability of the linguistic q-rung orthopair fuzzy Choquet integral approach. This research findings show that the united consolidation center is the most sustainable strategy for organizing urban parcel delivery.

Location-allocation combining fuzzy analytical hierarchy process for waste to energy facilities siting in developing urban areas: The case study of Lomé, Togo

Waste facilities siting is one of the complex problems encountered by decision makers of waste management in urban developing areas. Waste to Energy (WtE) facilities siting alongside transfer stations involves a Multi-Criteria Decision Analysis (MCDA) and has leveraged waste value chain. However, the process requires to consider the interlinked fields of environment, socio-cultural and economic/technical factors as Geographical Information Systems (GIS) in a context of lack of knowledge and expertise. This study aims to propose a framework of WtE facilities siting through a GIS-based Fuzzy Analytical Hierarchy Process (FAHP) and location-allocation method in Lomé’s case study in Togo. This method was applied with boolean logic and fuzzy overlay operators, to assess the potential sites and optimize their selection through a location-allocation solver considering transfer stations and road networks under ArcGIS. Moreover, WtE technologies were attributed to sites based on the territorial aspect. As result, 30.70% of the study area was excluded and three potential areas with a minimum value of 3.47 km2 comprised between 0.81% and 1.01% of the study area, and have been obtained with an acceptable consistency ratio of 0.09. The potential sites are more influenced by slope and residence criteria under economic/technical and socio-cultural factors, with 29.13% and 19.84% of weight respectively. Therefore, through the location-allocation method two optimized sites are obtained and assigned to transfer stations; the first suitable site close to industrial area is appropriate for the gasifier which consequently classified ahead of the anaerobic digester that is suitable for the second suitable site close to agricultural area. As result, prioritizing WtE technologies and site selection should take into account territory aspect, waste sources as well other environmental, socio-cultural and economic/technical factors. This approach has demonstrated its robustness and serves as a stepwise tool for decision makers in WtE facilities siting in developing urban areas.

Classification of renewable energy and its sources with decision-making approach based on bipolar complex fuzzy frank power aggregation operators

The sources for renewable energy are those that can never be depleted as these sources are given by nature. For instance, biomass, solar, wind, hydro, etc. They don't damage the climate as they generate practically zero pollution. The utilization of energy is expanding because of the development of the total populace. In this mechanical age where every one of the gadgets works by employing energy, the new mankind challenge is giving adequate measures of energy. Hence the basic theme of this article is to study the energy crisis and combat global warming through renewable energy and investigate the most superb source of renewable energy under the setting of bipolar complex fuzzy (BCF) Frank power aggregation operators (AOs). That's why, we deduce bipolar complex fuzzy Frank power averaging (BCFFPA), bipolar complex fuzzy Frank power weighted averaging (BCFFPWA), bipolar complex fuzzy Frank power ordered weighted averaging (BCFFPOWA) bipolar complex fuzzy Frank power geometric (BCFFPG), bipolar complex fuzzy Frank power weighted geometric (BCFFPWG), and bipolar complex fuzzy Frank power ordered weighted geometric (BCFFPOWG) operators. Furthermore, we deduce an approach to decision-making (DM) based on the deduced operators. The proposed approach of DM is a powerful technique for DM in circumstances, where the information is two dimensional along with positive and negative aspects of the element or object. It can provide a more accurate and nuanced view of the DM approach, which can lead to better decisions and better results. Afterward, we investigate the sources of renewable energy and discover the better and finest source of renewable energy by considering the artificial data in the structure of BCFS and utilizing the proposed approach of DM. To reveal further advantages and domination of the deduced operators and approach of DM, we compare the deduced operators and DM approach with a few prevailing theories.