Fuzzy Decision Support Research Articles

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

Двохвильова поляриметрична система відтворення та оцінювання анізотропії та дихроїзму плівок жовчі при діагностиці патологій

A system of two-wave polarization reproduction of distributions of phase and orientation parameters of anisotropy and coefficients of linear and circular dichroism of bile films in the diagnosis of pathologies has been developed. Fuzzy decision support models were derived and the reliability of the diagnosis of type 2 diabetes using the developed system was evaluated.

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.

An Intuitionistic Fuzzy Decision Support for Agile Project Selection Problem

In the early 1990s, organizations started to use information technology and software engineering, which encouraged the companies for employing project management methodologies to survive and then advance in competitive technologic environment. Use of project management methodologies provides efficient planning, budgeting and scheduling processes and high management quality for companies. A lot of different methodologies were developed and used to reach better ways of defining the project requirements, analyzing the problem, and implementing it in a systematic manner. In 2001, agile project management methodology came forward in response to cope with waterfall project methodology’s limitations that arise from unpredictability of technology evolution, customer requirements, and unstable business environments. In this study, intuitionistic fuzzy COPRAS method, which aims to obtain a solution relative to the ideal solution, is used to rank agile project alternatives and identify the best performing one among them. Intuitionistic fuzzy sets are used to deal with loss of information and hesitation in data that may occur in operations with fuzzy numbers. The application of the proposed intuitionistic fuzzy decision-making approach is illustrated by conducting a case study.

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.

Modified Bioinspired Method for Decision-Making Support for Prevention and Elimination of the Emergencie's Consequences

The work is devoted to solving the scientific problem of decision support for the prevention and elimination of emergency situations (ES) consequences based on fuzzy logic and machine learning methods. The relevance of this problem is due to the need to optimize the risk of adverse effects on human health and the environment in connection with emergency situations. The increased complexity of the tasks solved within the framework of the designated scientific problem is associated with the presence of information uncertainty in the complex accounting of heterogeneous characteristics, which in some cases can’t be normalized and brought to a single measurement scale. In such conditions, information processes for predicting the occurrence of potentially dangerous chains of events in the technogenic and natural spheres must be built using artificial intelligence methods and fuzzy logic to increase the efficiency of choosing the sequence of actions performed on the available information in order to build the necessary models, methods and algorithms to eliminate negative development situations and ensure monitoring of emergencies potential development cases. The authors give formalized statements of the tasks to be solved. A conceptual data model is proposed for constructing fuzzy decision support rules for the prevention and elimination of emergencies’ consequences. One of the options for formalizing such a data model is the transition to a vector representation of the information space. This will allow in the future to solve the problem of their classification on a set of information elements for distribution by classes of emergency situations. The criterion for evaluating belonging to a certain class is the argument for minimizing the distance between information elements in the vector space. The procedure for the accumulation by an intelligent system of precedent models set for the prevention or elimination of the emergencies consequences, which is a stage of machine learning, is described. After passing it, the intelligent system becomes capable of assessing the semantic similarity of operationally obtained models with precedents that have already fallen into the category of templates. The criterion for evaluating the effectiveness of an intelligent decision support system is the semantic similarity of the operational situational emergency model and the precedent model. A heuristic algorithm for determining semantic proximity was proposed. In order to optimize the time spent on supporting decision-making on the prevention and elimination of the emergency situations consequences, the authors also propose to use decentralized bioinspired methods, the advantages of which are internal procedures that provide diversification of the search space to exit from local optima and quickly obtain quasi-optimal solutions to the problem. The development of a modified bacterial optimization method (MMBO) was described. A software application has been created to conduct a computational experiment. The results of the conducted studies confirmed the advantages of the bacterial optimization proposed modified method.

A fuzzy decision support model for online review-driven hotel selection by considering risk attitudes of customers

Online hotel reviews provide a vital information source for customers to select an optimal hotel, but a large amount of vague and unstructured information increases the difficulty of decision-making. From the perspective of customers’ risk attitudes, this paper proposes a novel fuzzy decision support model for hotel selection based on online reviews. Firstly, the useful information from online reviews is extracted by attribute extraction and sentiment analysis, and then this information is aggregated into the Probabilistic Linguistic Term Set (PLTS) by considering the weight of each review. Secondly, the improved linguistic scale functions are constructed from the perspective of customers’ risk attitude to convert PLTS into quantitative information. Thirdly, an integrated attribute weighting method is presented based on objective weights of the statistical measure and subjective weights of the Analytic Hierarchy Process (AHP) technique. Fourthly, an extended Combinative Distance-based Assessment (CODAS) method is developed to evaluate the performances of hotels. The effectiveness of the proposed model is verified by the practical case from TripAdvisor.com and the comparative analysis with the existing methods.

A fuzzy decision support model for the evaluation and selection of healthcare projects in the framework of competition.

Our research aims to support decision-making regarding the financing of healthcare projects by structural funds with policies targeting reduction of the development gap among different regions and countries of the European Union as well as the achievement of economic and social cohesion. A fuzzy decision support model for the evaluation and selection of healthcare projects should rank the project applications for the selected region, accounting for the investor's wishes in the form of a regional coefficient in order to reduce the development gap between regions. On the one hand, our proposed model evaluates project applications based on selected criteria, which may be structured, weakly structured, or unstructured. On the other hand, it also incorporates information on the level of healthcare development in the region. The obtained ranking increases the degree of validity of the decision regarding the selection of projects for financing by investors, considering the level of development of the region where the project will be implemented. At the expense of European Union (EU) structural funds, a village, city, region, or state can receive funds for modernization and development of the healthcare sector and all related processes. To minimize risks, it is necessary to implement adequate support systems for decision-making in the assessment of project applications, as well as regional policy in the region where the project will be implemented. The primary goal of this study was to develop a complex fuzzy decision support model for the evaluation and selection of projects in the field of healthcare with the aim of reducing the development gap between regions. Based on the above description, we formed the following scientific hypothesis for this research: if the project selected for financing can successfully achieve its stated goals and increase the level of development of its region, it should be evaluated positively. This evaluation can be obtained using a complex fuzzy model constructed to account for the region's level of development in terms of the availability and quality of healthcare services in the region where the project will be implemented.

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 Generic Fuzzy Decision Support Model based on Plant Computational Model for Agriculture Investment Decision

A Generic Fuzzy Decision Support Model based on Plant Computational Model for Agriculture Investment Decision

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

A Simple Fuzzy Decision Support Model for Determining the Indonesian Green Cities

Green city is every bodies’ expectation. Every country highly competes to increase the number of green city. Thus several types of measurement standard for city green level appear and being practically implemented. The study’s objective is to develop a decision support model (DSM) for determining the Indonesian green cities based on the main method fuzzy logic. At this juncture, the Indonesian green city determined by green score of each city in Indonesia. The main method fuzzy logic interconnected to statistics and mathematics approaches. Slope calculation that combined with fuzzy logic conception was operated to treat three of five parameters believed in the model. The model design was delivered by using the object oriented approach and the model itself constructed via programming language Python in Google Colab platform. Finally, the constructed model is able to exhibit a hundred Indonesian cities’ green scores.

A New Approach to Artificial Intelligent Based Three-Way Decision Making and Analyzing S-Box Image Encryption Using TOPSIS Method

In fuzzy artificial intelligent decision support systems, three-way intelligent-decision making (TWIDM) has played a very important role in ranking objects under the double hierarchy linguistic variable (DHLV). The 8 × 8 S-boxes are very important for image encryption in secure communication. Therefore, the aim of the present study is to develop a new approach to artificial intelligent three-way decision making via DHLV and apply it to S-box image encryption. Artificial intelligent based three-way decision-making problems with double hierarchy hesitant linguistic terms are developed. The first and second hierarchy hesitant linguistic term sets make up the double hierarchy hesitant linguistic term set, which allows for more flexible expressions of doubt and fuzziness. First, we define the Einstein operational laws, score function, and Einstein aggregation operators; i.e., double hierarchy hesitant linguistic Einstein weighted averaging and weighted geometric operators. First, the unknown weight vector for decision experts is determined by using aggregation operators and entropy measures for DHLV. Then, we find the weight vector for our criteria by using the distance measure. In TWIDM, conditional probability is determined by using the extended TOPSIS method for evaluating the S-boxes for image encryption. The expected losses are then computed by aggregating the loss functions with the help of Einstein-weighted averaging aggregation operators. Finally, we apply the minimum-loss decision rules for the selection of S-box to image encryption. The proposed decision technique has been compared with existing three-way decisions and the result of proposed three-way decision making for analyzing and ranking the S-box is very good and reliable for decision making.

Measuring sustainability, resilience and livability performance of European smart cities: A novel fuzzy expert-based multi-criteria decision support model

Cities of the 21st century are buffeted with challenges, leaving potentially serious consequences on the future of urban living. Smartening development in cities has reinvented hopes of melting down predicaments in the early 2000s'. However, perplexed by the intensifying complexities of smart cities, urban living in smart cities needs to be evaluated with multiple conflicting criteria. Multi-criteria-based evaluations have been an answer to this case when attempting to gauge the composite performance of multiple decision-making entities. Several multi-criteria assessment techniques exist when dealing with selection problems. Nonetheless, the vagueness associated with the methodologies accompanied by uncertainties and complexities is inevitable in multi-attribute assessments. Fuzzy-based multi-criteria models are often an answer to such uncertainties when modelling real-world problems. The study thus presents a novel fuzzy expert-based multi-criteria decision support model, where the Analytical Hierarchy Process (AHP) is combined with the Evaluation based on Distance from Average Solution (EDAS) approach under a spherical fuzzy environment to create a composite index for comprehensive performance monitoring. The case of 35 high-tech European cities was used to empirically validate the proposed novel approach and thus construct a composite index. The composite index considers the intricate facet of integrating the concept of smart cities with sustainability, urban resilience, and livability under a unified framework. The fuzzy c-means partitioning technique was then used to segment smart cities into high, medium, and low-performing classes. A comparative analysis considering several distance-based approaches under a fuzzy environment with the SF-AHP and EDAS methodology is conducted to validate the robustness and stability of the proposed novel decision support model. The results revealed London as the top-ranked smart city that promotes sustainability, resilience, and livability in its current urban development model. Dusseldorf, Zurich, Munich, Oslo, Dublin, Amsterdam, Hamburg, Rome, Moscow, and Stockholm were no exemption from addressing the tritactic goals of sustainability, urban resilience, and livability well into their urban development plan and were placed in the high-performance cluster. The proposed model is efficient to express decision makers' preferences in a larger space and modelling functional parameters including hesitancy independently in the 3-dimensional domain. The model supports decision-makers and relocation analysts to assess the performance of smart cities and set targets to improve performance to remodel urban development to a more sustainable, resilient, and livable pattern.