Fuzzy Decision Support System Research Articles

Design and Development of Middle-Aged and Elderly Tourism Products Based on Fuzzy Decision Support System

Tourism product design and development is anxious with intricate decision-making processes that need to strike a balance between many criteria, goals, and limitations. In today's diversified and ever-changing tourist business, there are substantial sprints in developing and implementing products that appeal to middle-aged and older travellers. In this research, the utilization of fuzzy decision support system (FDSS) is investigated to personalize travel experiences for the elderly based on their unique interests and requirements. The main objective of this study is to reduce the complex tourism business patterns. This study presents a systematic framework for middle-aged and senior tourism products based on the literature on tourism trends, FDSS, and product creation in the tourism industry. This study proposes a middle-aged and elderly tourism product based on fuzzy decision support system (METP–FDSS) that employs fuzzy logic. This approach takes into account cultural sensitivities, accessibility concerns, travel preferences, and demographic data to create tourism experiences that are welcoming and immersive for older tourists. Through the application of fuzzy logic concepts, FDSS provides a flexible and robust decision-making platform that can aid in the development of personalized, inclusive, and immersive tourist experiences for the elderly. Customer happiness, accessibility, and the decision support system's ability to steer product development are all highlighted in the proposed METP–FDSS framework. Research on tourism among the middle-aged and the elderly also benefits from understanding user experience, cost effectiveness, competitive advantage, and social and environmental impact. While navigating the complexity of today's tourism sector, this study recommends using fuzzy logic as a decision support system to provide one-of-a-kind experiences for older and middle-aged tourists.

A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management

The phenomenon known as urban heat islands (UHIs) is becoming more common and widespread, especially in large cities and metropolises around the world. The main cause of these temperature variations between the city center and the suburbs is the replacement of large tracts of natural land with artificial (built-up) surfaces that absorb solar heat and radiate it back at night. UHIs have been the subject of numerous studies, most of which were about defining the main characteristics, factors, indexes, etc., of UHIs using remote sensing technologies or about determining mitigating activities. This paper provides a comprehensive overview of the literature, as well as a bibliometric analysis, to discover research trends related to the application of decision support systems and multi-criteria decision-making for UHI management, with a special emphasis on fuzzy theory. Data collection is conducted using the Scopus bibliographic database. Throughout the literature review, it was found that there were not many studies on multi-criteria analysis and decision support system applications regarding UHIs. The fuzzy theory application was also reviewed, resulting in only a few references. However, this topic is current, with an increase in published papers, and authors see this as an opportunity for improvement and further research.

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.

English Speaking Teaching Strategies Based on Natural Language Processing

The study of cognitive processes in English teaching methods from the perspective of HPM (History and Pedagogy of Mathematics) provides valuable insights into how students learn and understand the English language. Cognitive research in language learning focuses on the mental processes involved in acquiring, processing, and using language skills. By incorporating the principles of HPM, which examines the historical development and pedagogical approaches in mathematics education, researchers can draw parallels and apply similar methodologies to English language instruction. This paper constructed Bloom’s taxonomy Fuzzy Decision Support System (BTF-DSS). Bloom’s taxonomy provides information about the teacher’s understanding towards the teaching. Through BTF-DSS model offers teachers provides the knowledge and expertise for effective teaching with the fuzzy model. The BTF-DSS model uses the fuzzy logic decision support system for the appropriate significant teaching and learning experience. The simulation analysis of the BTF-DSS model expressed that the fuzzy model exhibits significant performance for the English teaching method in China through the HPM process.

An augmented fuzzy decision support system to analyse compatible cosmetic face masks for various complexions

AbstractBeauty face masks (BFM) are becoming increasingly popular among both men and women since they provide quick refreshment and nurture the skin. Given the wide range of skin types and the chemicals used in their formulation, it can be difficult to find a product that not only complements the skin type but is also free of potentially harmful ingredients that could endanger the consumer's health. When dealing with ambiguous situations, the multi‐attribute decision making (MADM) approach combined with fuzzy set theory is more effective. Type‐2 fuzzy sets (T2FS) provide greater flexibility in dealing with uncertainty in real‐world issues since they are characterised by a main and secondary membership function. In this research, we present the innovative idea of type‐2 linear diophantine fuzzy set (T2LDFS) as an intriguing tool for capturing expert reluctance about an issue. For analysing the discussed problem, a hybrid fuzzy VIKOR enhanced with the proposed fuzzy logic is suggested. A sensitivity and comparative analysis is carried out to establish the validity of the recommended approach.

Enhanced Solutions to Intuitionistic Fuzzy Multiobjective Linear Fractional Optimization Problems via Lexicographic Method

Optimization involving fuzzy numbers generally, and intuitionistic fuzzy numbers particularly is more and more an essential part of any computational intelligence method; and might be of interest in modeling fuzzy control systems, or carrying out a fuzzy sensitivity analysis. The recent literature includes many research papers related to both theoretical modelling, and practical implementation of fuzzy decision support systems. Fuzzy optimization is one of the main part in such decisionmaking tools. A realistic solution to a fuzzy optimization problem is always desired, and similarly, a Pareto optimal solution to a multiple objective optimization problem is always a need. In this paper we analyze the shortcomings; and eliminate the weaknesses of a solution approach from the literature proposed by Moges et al. in 2023. Firstly, Moges et al. used an accuracy function to de-fuzzify the original intuitionistic fuzzy optimization problem; then linearized the obtained crisp problem; and finally involved fuzzy goals to solve the derived multiple objective linear problem. We present two faulty key points of their approach, namely the de-fuzzification and linearization steps; prove the inappropriateness of their results; and propose some improvements. The final aim in addressing the first key point is to clarify the border between defuzzifications made in accordance to true theoretical statements, and those made for modeling reasons. The methodological improvement we propose is related to the second key point and it assures that Pareto optimal solutions - highly required in multiple objective optimization - are obtained. To illustrate our point of view, we use a numerical example from the literature, and report better numerical results derived by the improved methodology.

Implementation of FDSS (Fuzzy Decision Support System) Sugeno Model in Optimizing Bandwidth Requirement Management of Web-Based Networks

To increase the efficacy of bandwidth allocation at PT. Digdaya Monokrom Group, this study describes the development of a Fuzzy Decision Support System (FDSS) utilizing the Sugeno methodology. The Waterfall development process is employed for the purposes of system planning, construction, and maintenance. The study consists of three primary stages: the creation of fuzzy sets, the development of fuzzy rules, and the process of defuzzification. The study findings demonstrate that the utilization of FDSS has effectively improved the distribution of bandwidth. The distribution has shifted from a uniform one to a more optimized allocation, focusing on the Execution, Content Creator, Administration, and Research Team departments. During a four-week monitoring period, modifications were implemented to distribute bandwidth based on the preferences and needs of various departments, while adhering to the limitations of the current broadband subscription. This has enhanced the efficient exploitation of network resources. The research findings highlight the efficacy of FDSS in prioritizing resource allocations according to specific departmental requirements, consequently improving service quality and maximizing bandwidth subscription capacity. This demonstrates the implementation of strategic management methodologies to optimize the allocation of network resources, resulting in enhanced organizational efficiency and production.

Fuzzy Decision Support Systems for Selection of NEA Detection Technologies Under Non-Linear Diophantine Fuzzy Hamacher Aggregation Information

Fuzzy Decision Support Systems for Selection of NEA Detection Technologies Under Non-Linear Diophantine Fuzzy Hamacher Aggregation Information

A fuzzy decision support system for evaluating Lean-Green and company-specific production system.

Lean production is regarded as systematic solution for identifying and eliminating waste through continuous improvement of manufacturing processes to meet customers' requirements. Due to rising environmental concerns and the significant environmental impact of companies operations, issues such as environmental management and green production are on board agendas. In essence, green production seeks to reduce the environmental impacts of business processes. In order to examine green production, an original equipment manufacturer in the supply chain of tractor manufacturing industry is selected for this work to enable a focus on lean production aspects and its specific requirements to evaluate a company-specific production system (XPS). The results suggest that the company utilizes six lean-green production criteria including suitable and green packaging, quality improvement, waste reduction, environmental pollution reduction, transport management and additional transportation reduction, and cost reduction. Furthermore, continuous improvement and integrated auditing are selected as two critical and fundamental elements with the highest ranks, regarding the relationships between the XPS elements and lean-green production criteria.

Retracted: Complex Spherical Fuzzy Decision Support System Based on Entropy Measure and Power Operator

Retracted: Complex Spherical Fuzzy Decision Support System Based on Entropy Measure and Power Operator

Pyrrhic victory? Towards a decision support system for simulation projects in industrial practice based on Fuzzy cognitive mapping

Simulation has become a popular methodology to solve complex non-linear problems in research and practice. Yet, particularly in industrial practice, assessing the individual applicability of simulation projects remains an intricate, time-consuming and precarious challenge. This paper models simulation applicability by tapping into expert knowledge of simulation engineers with multiple years of simulation project experience. We decompose simulation applicability into a number of interconnected concept variables, which ultimately control the likelihood of project success and failure. The system is modeled in the form of a Fuzzy Cognitive Map, in which the nodes represent positive or negative project influencers in variables and link their interconnectedness. In doing so, this paper is the first to exceed the scope of simulation success factors or systemic risk assessment by addressing the need to provide holistic decision support on the context-dependent applicability of simulation projects. We contextualize organizational, environmental and methodology-related cause-effect relationships to develop a Fuzzy Decision Support System (FDSS) prototype that can guide scholars and practitioners when planning to adopt a simulation project for industrial purposes. The approach is illustrated using three applications, where the assessment has been conducted for (1) a large, (2) a medium-sized, as well as (3) a small organization, and evaluated through sensitivity analyses and a comparative survey. Ultimately, our study contributes to existing knowledge on success and risk factors for simulation projects, extends the realms of FCM to the case of simulation projects and sets a starting point for additional research on the context-specific applicability of the simulation methodology.

Evaluating the blockchain-based healthcare supply chain using interval-valued Pythagorean fuzzy entropy-based decision support system

In the current era, blockchain technology has emerged as a novel technique to maintain the operations of healthcare management systems. The global COVID-19 disease has led to increase in the utilization of technology for healthcare supply chain, patient data management, and claims settlement. Data management in healthcare industry is a complex procedure where multiple organizations offer appropriate supply chain facilities in everyday life. Inadequate data handling disrupts the supply chain, which has a longstanding impact on the healthcare region. Blockchain-based solutions are renowned for their ability to produce safe and traceable results and effective in the health sector for secure data retrieval and storage, resulting in more effectual product creation and tracking. These technologies can deliver data provenance, sponsors candid healthcare industry demands, and certifies the immutability of multi direction transactions. Due to involvement of multiple factors/criteria, the selection of an effective blockchain platform-based healthcare supply chain can be treated as a decision support system (DSS) problem. Thus, the objective of this paper is to present a novel interval-valued Pythagorean fuzzy DSS for evaluating the blockchain platforms for healthcare supply chain and selecting a suitable one with respect to multiple criteria and uncertainty. The proposed DSS framework is divided into three stages. First, some fairly aggregation operators are presented to combine the individual’s information and also discussed their desirable characteristics on interval-valued Pythagorean fuzzy sets (IVPFSs). Second, an integrated weighting tool is developed using entropy-based tool for objective weight of criteria and pivot pairwise relative criteria importance assessment (PIPRECIA) model for subjective weight of criteria with IVPFSs. For this purpose, new entropy is presented on IVPFSs. Third, the multi-attribute ideal-real comparative analysis (MAIRCA) model is developed with the combination of fairly aggregation operators, entropy-based weighting tool and PIPRECIA model with interval-valued Pythagorean fuzzy information. Further, the proposed DSS is used on a case study of blockchain platforms for healthcare supply chain evaluation, which confirms its applicability and usefulness. Sensitivity and comparative assessments are carried out to check the consistency, robustness and efficiency of the presented method.

Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems

Electroencephalograms (EEG) is used to assess patients' clinical records of depression (EEG). The disorder of human thinking is a very complex problem caused by heavy-duty in daily life. We need some future and optimal classifier selection by using different techniques for depression data extraction using EEG. Intelligent decision support is a decision-making process that is automated based on some input information. The primary goal of this proposed work is to create an artificial intelligence-based fuzzy decision support system (AI-FDSS). Based on the given criteria, the AI-FDSS is considered for classifier selection for EEG under depression information. The proposed intelligent decision technique examines classifier alternatives such as Gaussian mixture models (GMM), k-nearest neighbor algorithm (k-NN), Decision tree (DT), Nave Bayes classification (NBC), and Probabilistic neural network (PNN). For analyzing optimal classifiers selection for EEG in depression patients, the proposed technique is criterion-based. First, we develop a general algorithm for intelligent decision systems based on non-linear Diophantine fuzzy numbers to examine the classifier selection technique using various criteria. We use classifier methods to obtain data from depression patients in normal and abnormal situations based on the given criteria. The proposed technique is criterion-based for analyzing optimal classifier selection for EEG in patients suffering from depression. The proposed model for analyzing classifier selection in EEG is compared to existing models.

Emergency shelter materials under a complex non-linear diophantine fuzzy decision support system

AbstractThe distribution of emergency shelter materials in emergency cases around the world is a hard task, the goal of this research is to offer a Complex Non-linear Diophantine Fuzzy (C-NLDF) decision-making model for earthquake shelter construction. Essentially, the article is divided into three sections to acquire acceptable and precise measures in emergency decision-making situations. First, we present the Complex Non-Linear Diophantine Fuzzy Set (CN-LDFS), a new generalization of the complex linear Diophantine fuzzy set (CLDFS) and q-linear Diophantine fuzzy set (q-LDFS), as well as explore its key aspects. Furthermore, aggregation operators are useful for aggregating uncertainty in decision-making issues. As a result, algebraic norms for CN-LDFSs are produced based on certain operational laws. In the second section of the work, we offer a series of averaging and geometric aggregation operators under CN-LDFS that are based on defined operating laws. In the final section of the work, under complex Non-linear Diophantine fuzzy information, the ranking algorithms based on suggested aggregation operators are present to address the case study regarding emergency situation of earthquakes. In comparison section, results of existing and proposed operators explore the effectiveness of proposed methodologies and provide accurate emergency measures to address the global uncertainty about the construction of emergency shelters in earthquakes.

Analysis of obstacles to adoption of solar energy in emerging economies using spherical fuzzy AHP decision support system: A case of Pakistan

Adopting suitable energy policies that consider the existence of main obstacles could make the development of solar energy systems much smoother. Several obstacles have been placed in the way of solar energy development in Pakistan, which has prevented its growth from reaching a satisfactory level. In order to tackle these issues, it is necessary first to recognize the obstacles that stand in the way of implementing solar energy. So, this research aims to find and rank the obstacles to the expansion of solar power in Pakistan using a novel spherical fuzzy analytical hierarchy process. The results revealed that the economic obstacles category (21.46%) ranks highest among major categories. In contrast, the overall global ranking of sub-obstacles showed that budget constraints (4.68%), lack of access to credit/capital (4.52%), political instability (4.51%), high investment risk and operation cost (4.42%), and partnership issues (4.37%) are the more critical five sub-obstacles than the rest of the twenty-one obstacles within different categories. In addition, recommendations are made for the elimination of the obstacles. The current study has policy implications for policymakers, researchers, and practitioners involved in the solar sector in the country. Furthermore, it may help develop strategies for the smooth deployment of solar energy in Pakistan.

An Industry 4.0 implementation of a condition monitoring system and IoT-enabled predictive maintenance scheme for diesel generators

In most business and residential organizations, Diesel Generators (DG) is a viable supplementary power source for ensuring an undisturbed power supply. The DG is a hybrid machine that generates electrical energy using a Diesel Engine (DE) and an Electric Generator (EG). By routinely monitoring crucial machine parameters, alternative power source efficiency can be improved. Furthermore, Condition Monitoring Systems (CMS) based on the Internet of Things (IoT) have supplanted the traditional equipment maintenance method. Predictive maintenance is also an important building block of Industry 4.0, whose entire process and performance can be fully understood by using IoT-enabled Remote Monitoring (RM) schemes. Firstly, this paper introduces a remote monitoring and data acquisition scheme to realize the concept of predictive maintenance. Secondly, this article discusses a strategy for real-time observation of DG parameters as well as a comprehensive analysis of various metrics. Thirdly, this research article includes a monitoring and analysis scheme of crucial factors in a DG, like the speed of an engine, voltage output, the current produced, power factor, coolant required, fuel consumption, and battery health. Different mathematical models are formulated by correlating experimental data and estimating the coefficients. Finally, to create suitable real-time warnings under critical circumstances, a fuzzy logic-based Decision Support System (DSS) and web-based integration elements are presented.

Complex interval-valued intuitionistic fuzzy decision support system with application to COVID-19 healthcare facilities

The complex interval-valued intuitionistic fuzzy set (CIIFS), as an extension of the complex intuitionistic fuzzy set (CIFS), is one of the better tools for dealing with time-periodic information in a single set of two-dimensional information. So, this paper makes some new operators based on Aczel-Alsina’s t-norm and t-conorm in the CIIFS environment. First, some Aczel-Alsina t-norm and t-conorm operational laws are defined for complex interval-valued intuitionistic fuzzy numbers (CIIFNs). Then, using the proposed operational laws, Aczel-Alsina The operators Complex interval-valued intuitionistic Aczel-Alsina weighted average (CIIFAAWA), Complex interval-valued intuitionistic Aczel-Alsina weighted geometric (CIIFAAWG), Complex interval-valued intuitionistic Aczel-Alsina ordered weighted average (CIIFAAOWA), and Complex interval-valued intuitionistic Aczel-Alsina ordered weighted geometric (CIIFAAOWG) are developed. Further, some properties of the proposed operators are discussed and investigated in detail. A group decision-making method is also suggested based on developed operators in the CIIFS environment. The proposed operators are more adaptable and give more accurate results than existing ones. Furthermore, a multi-criteria decision-making technique is developed based on the proposed operators. A decision-making problem for healthcare facilities in public hospitals is provided to demonstrate the benefits and applicability of the proposed method. Finally, a comparative study is also investigated using the existing methods.

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.

A three-stage fuzzy neutrosophic decision support system for human resources decisions in organizations

This study proposes an integrated Decision Support System (DSS) with Multi-Criteria Decision-Making (MCDM) to evaluate trainers in organizations and choose the most suitable one(s) for a training program. The clustering stage determines the most appropriate number of trainees to be assigned to a trainer. The proposed model also considers the training budget and the constraint limiting the number of assignments. The proposed model has three stages: Delphi, the Interval-Valued Neutrosophic Analytic Hierarchy Process (IVN-AHP), and Fuzzy C-Means (FCM). The model’s input is expert opinions on various criteria, the candidate assessment score, and the bi-comparison of agreed criteria. Outputs are weights, and the members of each cluster represent possible candidates. We present a case study to demonstrate the applicability of the proposed DSS in a training program assessment. Additional applications of the proposed system include recruitment and promotion.

Retraction Note: Artificial intelligence enabled fuzzy multimode decision support system for cyber threat security defense automation

Retraction Note: Artificial intelligence enabled fuzzy multimode decision support system for cyber threat security defense automation