Fuzzy Decision Support System Research Articles

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Optimizing cloud service provider selection with firefly-guided fuzzy decision support system for smart cities

Businesses that want to benefit from cloud computing must choose a Cloud Service Provider (CSP). Cost, performance, Reliability, security, and SLAs must be evaluated during the decision process. CSP assessment is tough because of uncertainties and erroneous data. Fuzzy logic and the firefly optimization technique have been proposed in this paper to achieve optimal results based on diverse components. The proposed methodology uses consumer, service provider, and public reviews based on the three elements. These components' ratings can be used to analyze efficiency. Simple fuzzy logic is inferior to optimized fuzzy logic, according to experiments. The Firefly Optimized Fuzzy DSS is compared against non-optimized fuzzy decision-making systems and standard optimization methods. The results show that the proposed model is better for selecting the best CSP based on many parameters and managing assessment uncertainty. Fuzzy logic and optimization methods provide more nuanced and precise decision-making that accounts for subjective assessments and confusing facts. Businesses can make informed choices and ensure their CSP needs are satisfied with the approach. Finally, the Firefly Optimized Fuzzy Decision Support System offers a new perspective on cloud service provider selection by merging fuzzy logic with optimization. The system's ability to handle poor evaluations and ambiguity makes it ideal for CSP selection's complex decision-making process. This paper helps build decision support systems for choosing a cloud service provider and has substantial implications for firms seeking successful cloud computing solutions. This research work's conclusions have major implications for corporations and organizations searching for the finest cloud service providers. CSP-related real-world datasets are tested experimentally.

The Application of Big Data and Fuzzy Decision Support Systems in the Innovation of Personalized Music Teaching in Universities

Personalized music teaching in universities improves students’ learning and efficiency through adaptive guidance. This adaptability requires large study data and intelligent decisions based on the learner’s ability. This article introduces a Definitive Teaching Support System (DTSS) exclusive to music learning to augment this concept. This system is designed to increase the adaptability of music learning based on student interest and ability. The system is powered by a fuzzy decision system for identifying maximum teaching adaptability to personalized processes. Low-to-high-sorted personalization provides new endorsements for further music sessions in the fuzzy derivative process. Maximum adaptability is the target for new personalized sessions in the universities. This differs for various students from which a common adaptability level for monotonous recommendations is identified. The identified adaptability is set as a global maximum solution towards music learning personalization. The defuzzification reduces the chances of low adaptability by expelling the stationary adaptability outcomes.

Applications of Fuzzy Decision Support Systems in Human Resource Management by using TOPSIS approach

Applications of Fuzzy Decision Support Systems in Human Resource Management by using TOPSIS approach

A Fuzzy Decision Support System for Risk Prioritization in Fine Kinney-based Occupational Risk Analysis

Analyzing Occupational Health and Safety (OHS) risks requires prioritizing risk, a vital step in hazard and effect analysis (HEA). Currently, there is no existing approach to determine the priority of risk in HEA-based occupational risk analysis that incorporates interactive risk factors and spherical fuzzy risk information. This paper presents a novel approach to address the constraints by introducing a fresh framework for evaluating job-related hazards using HEA by combining the spherical fuzzy set (SFS), the multi-attributive border approximation area comparison (MABAC) technique, and the Ordered Weighted Averaging (OWA) operator, a more sophisticated approach is achieved within this framework. The SFS is utilized in this framework to depict the more ambiguous and unsure data given by specialists, offering a more efficient approach to handling the fuzzy risk information, encompassing the non-membership degree and hesitation. In addition, an advanced MABAC method is used to prioritize occupational risk. Afterward, we provide a practical instance of utilizing the MABAC technical-oriented risk prioritization approach for evaluating potential occupational dangers in risk analysis.

Fuzzy Decision Support Systems to Improve the Effectiveness of Training Programs in the Field of Sports Fitness

To maximize training programs and improve individual performance, the sports fitness profession is always looking for new and innovative solutions. Fuzzy decision support systems provide a strong basis for improving training regimen efficacy through flexible and adaptive decision-making. In sports fitness, doing things differently might affect the way one can train and even lead to injury. Particularly if exercises call for expert training regimens, determining if fitness requirements are sufficiently satisfied is a difficult task. Athletes participate in training programs focusing on strengthening their bodies and minds to perform better. Each sport has unique needs, and athletes should prepare to meet those demands while still meeting their sport’s general fitness activity requirements. A novel strategy for assisting athletes with fitness-related decision-making is presented in this study as the Fitness Mamdani Decision System. At the outset, the system provides an adaptive decision framework to improve the effectiveness of training programs by applying the principles of intuitionistic fuzzy numbers and fuzzy logic. The study wants to know how to train effectively, and it takes important factors like your mood, degree of preparation, sleep quality, and stress levels into account. Using language-specific terminology and triangle membership functions, the Mamdani fuzzy inference system generates rules based on analyzing these crucial elements. Regarding measures like adaptability index, training load capacity, long-term program efficiency, and participation ratio among sports fitness individuals, the system is guided by fuzzy rules that infer decisions.

Application of Fuzzy Decision Support Systems in Risk Assessment of Southeast Asian Labor Market

The labor market in Southeast Asia has several complicated issues, including the quick changes in the economy, the various behaviors of employees, and the shifting regulatory settings. To foster sustainable regional development and make decisions based on accurate information, a comprehensive risk assessment is important. Conventional methodologies, on the other hand, often fail to reflect the multifaceted personality of issues about the labor market. In light of recent economic changes and the complexity of applicable policies, this paper recommends a fuzzy decision support system to enhance risk assessment (FDSS-ERA) that could enhance risk assessment in Southeast Asian labor markets. The model methodically assesses hazards, using a mixture of fuzzy logic sense and decision assistance, incorporating changes in employment trends and demographics. The results highlight fuzzy logic’s role in decision-making for effective risk management and policy interventions, showing improved risk comprehension. While educated policy choice making can lead to equitable development in Southeast Asia’s labor work marketplace, FDSS is a potential method. In conclusion, to overcome the many obstacles that stand in the way of chance assessment in the labor market in Southeast Asia, FDSS provides a powerful strategy. Because it offers more advanced analytical tools, this research assists decision-makers in the region in developing more effective guidelines, proactively reducing risks, and achieving projects that promote sustainable development.

Using the Fuzzy Method and Multi-Criteria Decision Making to Analyze the Impact of Digital Economy on Urban Tourism

Urban tourism promotes the economic growth of a nation around the year through direct and indirect incomes. In recent years, the digital economy has impacted the growth of urban tourism through hassle-free money transactions and expenditures. This article, therefore, introduces a Multi-Criteria Fuzzy-based Decision-Making Method (MCFDMM) for validating the impact of the digital economy impact over tourism. The study introduces a new framework, DLFDSS-RRM, that uses deep learning and fuzzy decision support systems for residence right management, enhancing resource allocation, security, and resident satisfaction in urban residential communities. The criteria such as expenses, positive response, and repeated payments are validated by the tourists across their travel plan. These conditions satisfying the tourist’s expectations are estimated based on their reviews of economic conditions are validated. The validation is performed against the growth of the country from urban tourism. The fuzzy process validates the growth of the country between two successive financial quarters based on the above conditions. In the condition analysis, the fuzzy process identifies the least derivatives contributing to minimal economic growth. This is reversed using the hiking condition that occurs in any quarter and hinders economic growth. Therefore, the process is validated using the metrics growth rate, condition satisfaction, analysis rate, analysis time, and unrelated assessment. The comparative analysis across various models reveals growth rates ranging from 0.263 to 0.4055, condition satisfaction percentages from 53.747 to 74.351, and analysis rates from 0.275 to 0.4662.

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

Application of Fuzzy Decision Support System Based on GNN in Anomaly Detection and Incident Response Service of Intelligent Security

Application of Fuzzy Decision Support System Based on GNN in Anomaly Detection and Incident Response Service of Intelligent Security

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.