Application Of Fuzzy Model Research Articles

Enhancing Business Decision Making through a New Corporate Reputation Measurement Model: Practical Application in a Supplier Selection Process

The importance of corporate reputation is a critical issue for business growth, sustainability, and success, as it represents a key intangible asset for the management of all companies. This business importance has its correlation in the academic and research field, where corporate reputation has a high number of publications in the literature. However, despite the importance of this concept, one of the great challenges of recent decades, and one that is still evident today, is how to measure corporate reputation quantitatively and how it affects sustainability. Following an in-depth exploration of the available literature, this manuscript aims to demonstrate the effective application of fuzzy models in enhancing decision-making processes within the realm of corporate reputation management for companies. To achieve this goal, this paper proposes a new corporate reputation measuring model based on the fuzzy 2-tuple linguistic and AHP (Analytic Hierarchy Process) methodologies. The proposed model promotes the computation of corporate reputation for companies based on three widely cited and universally recognized criteria outlined in the literature, drawing inspiration from a well-established framework in the field. This approach ensures a comprehensive and widely accepted foundation for evaluating corporate reputation: Capability, Benevolence, and Integrity and adding the Net Promote Score variable. To integrate sustainability into this equation, our model suggests the inclusion of variables related to sustainable practices in the measurement of corporate reputation. Recognizing the growing importance of sustainability in the public perception of companies, factors such as social responsibility, environmental management, and business ethics are recommended for consideration in the assessment of corporate reputation. The model proposed in this paper is tested and validated on a real business case, based on the selection of several companies selected for an empirical study in the selection of suppliers. For future research endeavors, the authors suggest expanding the model to encompass various decision-making processes. Additionally, they recommend exploring the integration of machine learning algorithms and data analysis techniques to identify patterns and provide recommendations for enhancing corporate reputation.

Application of fuzzy model for the shelf-life determination of whole sea bass (Dicentrarchus labrax) under refrigerated temperatures

In this study, microbiological deterioration of whole sea bass stored at refrigerator temperatures (4°C) was determined through fuzzy modeling. Total Mesophilic Aerobic Bacteria (TMAB), Total Psychrophilic Aerobic Bacteria (TPAB), Lactic Acid Bacteria (LAB), Pseudomonas sp., and Enterobacteriaceae counts were observed during 12 days of storage. Quality Index Method (QIM) was used for sensory analysis of the samples and shelf life was determined according to sensory deterioration. The growth of the microorganisms was statistically significant (p<0.05) at the end of the storage period. Additionally, significant differences (p<0.05) were observed for QI scores after the 8th day of the storage in whole sea bass stored under refrigerated conditions. A total of 12 rules were determined in the fuzzy logic application. Mamdani was used as the fuzzy type for fuzzy modeling, and centroid was used as the defuzzification method. Fuzzy membership values were computed using triangular membership functions. According to the research results, the shelf life of sea bass fish was determined as 8 days from the time of 12 days storage. As a result, it was observed that the sensory score could be determined by using Fuzzy logic (R2=0.96) according to microbiological parameters in sea bass stored in refrigerator conditions. In this context, it was concluded that Fuzzy logic could be used in shelf-life estimation in seafood products.

MAPPING THE RISK OF FOREST FIRES IN THE RIO PRETO NATIONAL FOREST IN THE ATLANTIC FOREST

The Atlantic Forest biome is annually exposed to forest fires that damage thousands of hectares of forest, promote the formation of forest fragments, the destruction of biodiversity, soil compaction, runoff and silting of water bodies. Thus, the prediction and suppression of fire outbreaks are important to minimize the damage caused by fire. In this sense, this study aimed to analyze and model the risk of forest fires occurring in the Rio Preto National Forest and its buffer zone using the Fuzzy artificial intelligence technique. To do so, the land use and occupation, proximity to roads, slope and relief orientation variables were used to compose the model. Thus, the influence of each variable on burning episodes was determined with the aid of geographic information systems (GIS), as well as the spatial distribution of each of the risk classes (very low, low, moderate and high). a historical series of fires between the years 2010 and 2020 was used to perform a comparative analysis of the model. The results showed that the study area does not present worrisome risks regarding the occurrence of fires, since it is mostly covered by very low and low risk classes. Thus, it can be concluded that the application of Fuzzy modeling enables evaluating the spatial distribution of fire risk classes for the protected area areas, for which the proposed comparative analysis indicated the model’s effectiveness.

Application of fuzzy modelling to predict the earthquake damage degree of buildings based on field data

Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data.

Application of fuzzy modeling and response surface methodology for optimization of cadmium uptake by colpomenia sinosa

The optimum conditions for the removal of cadmium as heavy and toxic metals via a biomass were investigated in this program. The biomass was prepared from an eco-friendly, native, and low-cost algae microorganism, e.g., Colpomenia sinuosa. The cadmium uptake involved the biosorption process onto the cell wall of the Colpomenia sinuosa. The experiments were carried out on the five different parameters of temperature, algae biomass dosage, the initial cadmium concentration, pH of the cadmium solution, and contact time for interval times of cadmium of the biomass surface. The design of the experiment (DOE) was done for different conditions. The optimum conditions were compared via two optimization methods. Both the response surface methodology (RSM) and fuzzy modeling were treated with experimental data. The contour maps were planned for understanding the effects of two interactive factors. The combined effects of pH-temperature, pH-contact time, and algae biomass dosage-temperature were plotted for cadmium uptake.

Application of fuzzy modelling and Particle Swarm Optimization to enhance lipid extraction from microalgae

Lipid extraction from microalgae is maximized by defining the optimal operating conditions of the microwave pretreatment method. Using the experimental data, a robust model that describes the lipid extraction is generated using fuzzy logic. Then, the optimal extraction conditions of the lipid are determined using Particle Swarm Optimization (PSO) algorithm. Three different operating parameters influence on the recovered lipid from Microalgae. These parameters are power (W), heating time (min), and extraction time (h). Accordingly, during the optimization process, these parameters are used as a decision variables for PSO optimizer in order to maximize the recovered lipid that used as a cost function. The resulting plots demonstrated a well-fitting between the fuzzy model and the experimental data. Based on the built model, the optimization process achieved a significant increase in the lipid extraction by 22% compared to that obtained experimentally and using the ANOVA.

Application of fuzzy modeling in the evaluation of crisis state of enterprises

The features of the proposed fuzzy model for assessing the crisis state of enterprises have been disclosed. The MATLAB software environment has been selected as the environment for building a fuzzy output system. In the model of a fuzzy assessment of the crisis state of enterprises, the following input linguistic variables have been highlighted: the relative level of financial status, the probability of bankruptcy, the level of information security, the level of innovation potential. The terms of the input variables and the result variable have been described. The rule base for fuzzy inference system has been formulated. The results of modeling the assessment of the crisis state of enterprises have been represented by a fuzzy inference procedure.

Application of Fuzzy Modelling to Predict Construction Projects Cash Flow

Construction project managers are always looking for methods for forecasting future projects and preventing of potential delays in the project. One of the most crucial requirements of construction project managers and financial planners is awareness of project cash flow and financial status. On the other hand, the unique properties of construction projects with uncertainties such as activity duration, the variability of resources, material costs and also ambiguity in the employer’s payments are factors that have an effect on the correct prediction of project cash flow. Hence, the project team should examine project cash flow under uncertainty environment. There are many approaches for considering uncertainty such as fuzzy sets, interval theory, rough and grey system. But the most well-known approach is fuzzy sets which has wide applications in engineering and management. Hence in this paper, we proposed a new method for forecasting project cash flow under fuzzy environment. Finally, the proposed method was applied on an “Engineering, Procurement and Construction” (EPC) project and it is demonstrated that the proposed model has a high performance in the prediction of project cash flow.

Application of Fuzzy Logic for Monitoring of Appearance of Heat Waves in Large Towns

In the paper are introduced some results of the influence of cooling effect of vegetation on the climate in large towns. The results have been acquired from measurement of some meteorological variables in selected parts of large town and from application of fuzzy models on the prediction of maximum day temperature. Great motivation of the paper is not only course of maximum temperature in standard days but especially the more dramatic situations as is appearance of Heat Waves. Besides the selection of relevant variables and the design of knowledge based system (with application) is performed an approximation operation for knowledge based systém function taking into account the conditions throughout the city.

Применение нечеткого моделирования для информационной поддержки принятия решений при ликвидации лесных пожаров

Применение нечеткого моделирования для информационной поддержки принятия решений при ликвидации лесных пожаров

Применение нечеткого моделирования в транспортной сфере

Применение нечеткого моделирования в транспортной сфере

Fuzzy modelling to identify key drivers of ecological water quality to support decision and policy making

Water quality modelling is an effective tool to investigate, describe and predict the ecological state of an aquatic ecosystem. Various environmental variables may simultaneously affect water quality. Appropriate selection of a limited number of key-variables facilitates cost-effective management of water resources. This paper aims to determine (and analyse the effect of) the major environmental variables predicting ecological water quality through the application of fuzzy models. In this study, a fuzzy logic methodology, previously applied to predict species distributions, was extended to model environmental effects on a whole community. In a second step, the developed models were applied in a more general water management context to support decision and policy making. A hill-climbing optimisation algorithm was applied to relate ecological water quality and environmental variables to the community indicator. The optimal model was selected based on the predictive performance (Cohen’s Kappa), ecological relevance and model’s interpretability. Moreover, a sensitivity analysis was performed as an extra element to analyse and evaluate the optimal model. The optimal model included the variables land use, chlorophyll and flow velocity. The variable selection method and sensitivity analysis indicated that land use influences ecological water quality the most and that it affects the effect of other variables on water quality to a high extent. The model outcome can support spatial planning related to land use in river basins and policy making related to flows and water quality standards. Fuzzy models are transparent to a wide range of users and therefore may stimulate communication between modellers, river managers, policy makers and stakeholders.

Application of fuzzy modeling to detect mathematical learning disorder

Fuzzy logic has proved to be a powerful tool for decision-making, and to handle and manipulate imprecise and noisy data. Major advantage of this theory is that it allows the natural description, in linguistic terms, of problems that should be solved rather than in terms of relationships between precise numerical values. This paper discusses the use of fuzzy classification to cognitive science field for classifying children having mathematical learning disorder. We have developed a fuzzy model with six fuzzy attributes from six identified dyscalculia testing domains. The output of fuzzy model is classification of children as pure, probabilistic and no dyscalculic. Our findings have been scientifically supported by the neuropsychological results that the range of 3–6% of the population suffers from severe mathematical learning disorders.

Prioritization of Remedial Approaches for Dealing with Dutch Disease Consequences in Iran: an Application of Fuzzy-Modeling

During last decades, Iran's economy has been stricken by Dutch Disease. In order to apply appropriate remedial for dealing with this phenomenon, it should be explained by scientific principles and its impacts on macroeconomic variables are evaluated. In this regard, it is necessary to determine the ranking of proposed remedial policies. The objective of present study is to detect and classify main fields affected by Dutch Disease and propose remedial based on the comments of economists and experts. To achieve this purpose, a fuzzy decision-making approach is designed in three ranking models. In addition, the short-run, medium-term, and long-run scheduling procedures are considered as the key decision- making criterion. Results signify that the dominance degree of refinement of of National Development Fund is 3.79 dedicating that it is the first priority among the six alternatives. The next priorities are refinement of government budget improving the monetary refinement of of foreign trade sector, refinement the relative price mechanism, and refinement the of exchange rate whose dominance degrees are respectively 3.508, 3.283, 1.735, 1.582 and 1.096. It is concluded that among remedial policies, refinement of of National Development Fund and refinement of government budget policies are found as the most preferable in the viewpoints of economists. Obviously, implement of these remedial can work for treatment of the disease. Moreover, the results of sensitivity analysis evince that all three models proposed in this research are constant and not affected by threshold values.

SCOLS-FuM: A Hybrid Fuzzy Modeling Method for Telecommunications Time-Series Forecasting

An application of fuzzy modeling to the problem of telecommunications time-series prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on Subtractive Clustering (SC) and the Orthogonal Least Squares (OLS) techniques. Particularly, the SC is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, an orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. A comparative analysis with well-established forecasting models is conducted on real world telecommunications data, where the characteristics of the proposed forecaster are highlighted.

A rule-based system for trade-off among energy consumption, tool life, and productivity in machining process

With ever-increasing understanding of environmental and societal concerns, the focus of manufacturing industries, worldwide, is fast changing from mere profit-making to ensuring sustainability. The companies are striving hard to make their manufacturing processes more environment-friendly, in addition to being cost effective and time- and resource-efficient. The paper presents an experimental investigation and an application of fuzzy modeling for trade-off among energy consumption, tool life, and productivity of a metal cutting (machining) process. A total of 54 grooving experiments are performed under various pre-determined combinations of the workpiece material hardness, cutting speed, cutting feed, and width of cut. The respective measurements are taken for tool damage, energy consumed, and cutting and feed forces. A fuzzy rule-based system is developed that consists of two modules: optimization and prediction. The former suggests the most suitable settings for the cutting parameters that would lead to accomplishment of various combinations of the objectives related to energy consumption, tool life, and machining productivity. The prediction module works out the predicted values of all the responses based on the finalized values of the four input parameters.

Anytime Fuzzy Modeling

Nowadays practical solutions of engineering problems involve model-integrated computing. Due to their flexibility, robustness, and easy interpretability, the application of fuzzy models, may have an exceptional role. Despite of their advantages, the usage is still limited by their exponentially increasing computational complexity. Although, combining fuzzy and anytime techniques it becomes possible to cope with the available, usually imperfect or even missing information, the dynamically changing, possibly insufficient amount of resources and reaction time. In this paper, possibilities offered by (Higher Order) Singular Value Decomposition ((HO)SVD) based anytime fuzzy models are analyzed. A modeling methodology is suggested, which offers a way for both complexity reduction and improvement of the accuracy without complexity explosion thus coping with the temporarily available amount of information and (finite) time/resources and finding the balance between accuracy and complexity.

The Application of Fuzzy Modeling and PSO in Spaces Allocation of Naval Ship

A semi-automated general arrangement technique of naval ship is discussed, it approached by a series of two essentially two-dimensional problems: the allocation of spaces to Zone-decks (one deck within one vertical zone), after that, implement the detailed arrangement of spaces to each Zone-deck. In this context, fuzzy modeling is introduced for evaluating environmental loadings (noise, vibration and ship motion) and Naval Equipment Standards, and then these measures are translated to fuzzy utility values which express the satisfaction location of each group spaces. The fuzzy location preference values are combined with Zone-deck utilization utility to establish the objective function using fuzzy product inference. Lastly, the particle swarm optimization (PSO) is performed on the objective function to acquire the allocation plan of spaces in each Zone-deck.

Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

In this paper, an application of a neuro-fuzzy modeling approach is presented in order to characterize the essential behavior of enzymatic esterification processes. The accuracy of the developed model was validated by comparing the response of the model and actual experimental data. The simulation results showed good generalization of the proposed model and its ability to predict the reaction yield, where the error of prediction for training data was less than 3%, and for validating and testing data less than 3 and 1.5%, respectively. A model-based optimization was performed to obtain the best operating conditions by using genetic algorithm. A fair comparison between the optimization results obtained from simulation experiments and laboratory data indicated the accuracy and feasibility of the proposed approach for estimating the optimal profiles in biotechnological processes. This can further facilitate up-scaling of the process by selecting the appropriate combinations of potential manufacturing parameters.

Tableting process optimisation with the application of fuzzy models

A quality-by-design (QbD) principle, including process analytical technology, is becoming the principal idea in drug development and manufacturing. The implementation of QbD into product development and manufacturing requires larger resources, both human and financial, however, large-scale production can be established in a more cost-effective manner and with improved product quality. The objective of the present work was to study the influence of particle size distribution in powder mixture for tableting, and the settings of the compression parameters on the tablet quality described by the capping coefficient, standard deviations of mass and crushing strength of compressed tablets. Fuzzy models were used for modelling of the effects of the particle size distribution and the tableting machine settings on the tablet quality. The results showed that the application of mathematical models, based on the contemporary routinely measured quantities, can significantly improve the trial-and-error procedures.