Crisp Model Research Articles

Approximate Solution of Fuzzy Sedimentary Ocean Basin Boundary Value Problem Using Variational Iteration Method

The problem that is considered in this paper is developed from the migration of the coastline over time in sedimentary ocean basins that may be considered as a moving boundary problem with a variable latent heat transfer equation as a governing equation if one of the boundaries is unknown, with boundary and initial conditions are given. This model discusses the shoreline movement within sedimentary basins. The main objective of the paper is to use fuzzy logic to formulate this problem when modelling sand particles migration in saltwater by utilizing fuzzy numbers whenever replacing some of the problem's parameters. In reality, the proposed model is more general than the nonfuzzy or crisp models. By employing the variational iteration method, the approximate numerical solution of the proposed problem has been found, and computer software written in Mathematica 11 have been used to obtain the results.

Reliability dependent production-inventory model for redundancy allocation via fuzzy logic

This study deals with a reliability dependent production-inventory model in two scenarios: a redundancy allocation with crisp structure and the other with fuzzy logic. Here, a manufacturer purchases some raw-materials/components in variable cycles and arranges them as series-parallel system to produce a single item with production cost dependent on system reliability. In this model, a retailer gets the opportunity of warranty period and credit period offered by the manufacturer. Also, the retailer’s demand is dependent on system reliability, credit period and selling price. In the crisp model, the component reliability is exponentially dependent on known failure rate and failure time. However, there is no dependent relationship between these two parameters. Actually, the real world is full of uncertainty and these two parameters may depend on each other following some uncertain nature which can be expressed as fuzzy logic. So, in the fuzzy model, failure time has been considered as dependent on failure rate following fuzzy logic and these fuzzy relations are defuzzified by using three fuzzy inference techniques: Mamdani, Sugeno and Tsukamoto. Main goal of this article is to determine the optimum number of cycles and components to maximize manufacturer’s profit and system reliability with some constraints. The model is solved by using elitist non-dominated sorting genetic algorithm (NSGA-II) and some numerical examples closed to real-world have been executed. Comparative analyses are done for different cases; different fuzzy inference techniques and for active and mixed strategies. Finally, some sensitivity analyses and managerial insights are drawn.

Performance Analysis of Fully Intuitionistic Fuzzy Multi-Objective Multi-Item Solid Fractional Transportation Model

An investigation is conducted in this paper into a performance analysis of fully intuitionistic fuzzy multi-objective multi-item solid fractional transport model (FIF-MMSFTM). It is to be anticipated that the parameters of the conveyance model will be imprecise by virtue of numerous uncontrollable factors. The model under consideration incorporates intuitionistic fuzzy (IF) quantities of shipments, costs and profit coefficients, supplies, demands, and transport. The FIF-MMSFTM that has been devised is transformed into a linear form through a series of operations. The accuracy function and ordering relations of IF sets are then used to reduce the linearized model to a concise multi-objective multi-item solid transportation model (MMSTM). Furthermore, an examination is conducted on several theorems that illustrate the correlation between the FIF-MMSFTM and its corresponding crisp model, which is founded upon linear, hyperbolic, and parabolic membership functions. A numerical example was furnished to showcase the efficacy and feasibility of the suggested methodology. The numerical data acquired indicates that the linear, hyperbolic, and parabolic models require fewer computational resources to achieve the optimal solution. The parabolic model has the greatest number of iterations, in contrast to the hyperbolic model which has the fewest. Additionally, the elapsed run time for the three models is a negligible amount of time: 0.2, 0.15, and 1.37 s, respectively. In conclusion, suggestions for future research are provided.

A Study of an EOQ Model of Deteriorated Items with Pentagonal Dense Fuzzy Demand Rate

In this project work, we deal with an economic order quantity inventory model of deteriorating items under non-random uncertain demand. Here we consider the customers screen the fresh items during the selling period. After a certain period of time, the deteriorated items are sold at a discounted price. Firstly, we solve the crisp model, and then the model is converted into a fuzzy environment. Here we consider the pentagonal dense fuzzy, trapezoidal dense fuzzy, and triangular dense fuzzy for a comparative study. We have taken the numerical result using LINGO 18.0 software. Finally, sensitivity analysis and graphical illustration have been given to check the validity of the model.

AI-Based Cerebral Vascular Accident (CVA) Analysis and Prediction

Early stroke detection significantly increases the prognosis for both survival and rehabilitation. Patients are more likely to receive appropriate therapy that minimizes brain damage and lowers the risk of consequences if a stroke is detected early on. Researchers are motivated to investigate the possibilities of artificial intelligence and machine learning technologies in creating new categorization systems that can identify and detect strokes more quickly and accurately due to their rapid development. This could potentially enhance the likelihood of surviving and recuperating. The support-vector machine (SVM), logistic regression, decision tree, random forest, Bayes nets, and K-nearest neighbor (KNN) algorithms are employed in this study's CRISP model technique. To enhance the final quality, the dataset was balanced using an oversampling technique, and the algorithms employed were subjected to principal components analysis (PCA). With an accuracy rate of 99%, the Random Forest algorithm is regarded as the optimum for prediction. Our study illustrates that the random forest classification model using the data balancing strategy outperforms the other strategies investigated, with a 99% classification accuracy and a 98% F1 score. The study also shows that the outcomes are unaffected by preprocessing with the PCA technique. The next objectives of the study are to use a larger dataset, various preprocessing methods, and machine learning models to enhance the framework models.

An Inventory Model for Deteriorating Items with Stock-Dependent Demand under Fuzzy Lead Time and Partial Backlogging

For inventory management purposes, lead time refers to the amount of time it takes for a purchase order to be completed. Its effect is an important phenomenon in inventory management system. It also plays a significant role when lead time unknown to the decision makers. This paper deals with an inventory model for deteriorating items under fuzzy lead time. The stock dependent demand with partially backlogged shortages are considered in the proposed model. The total inventory costs for both crisp model and fuzzy model are derived. The fuzzy lead time is assumed to be triangular and trapezoidal numbers. The signed distance methods (SD) is used for defuzzification purpose. The developed model is validated with the help of numerical illustration under both crisp and fuzzy scenario. A pictorial presentation is furnished to explain the behaviour of the total inventory costs towards lead-crisp, lead-triangular and lead-trapezoidal values. Lastly a sensitivity analysis is performed to judge the sensitive behaviour of the total cost towards changes of the cost parameters.

To Solve Pythagorean Fuzzy Decagonal Transportation Problem Using Bipartite Graph with Optimal Solution

Objectives: This study aims to solve the Pythagorean fuzzy transportation problem by means of providing a novel approach. Methods: To determine the optimum decision-making, we applied the MODI approach. To illustrate the proposed approach, we applied several types of numerical issues. An algorithm for solving the suggested technique is described. Findings: This study presents a bipartite graph-based strategy to the Pythagorean fuzzy transportation problem. Applying TORA statistical software, the data analysis discovered that the crisp model becomes linear when the membership function is applied. Novelty: This study has paved a way for better optimal results to obtain when the Pythagorean fuzzy set is converted using the score function instead of the standard techniques for Defuzzification. Keywords: Score Function, Pythagorean Fuzzy, Defuzzification, Pythagorean Fuzzy Transportation Problem, Decagonal Fuzzy Number, Ranking Method, Initial Basic Feasible Solution, and Optimal Solution

Optimization techniques for crisp and fuzzy multi-objective static inventory model with Pareto front

Optimization techniques for crisp and fuzzy multi-objective static inventory model with Pareto front

A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location

This work focuses on a hybrid preference-based electric vehicle charging station location problem, which considers multiple optimization preferences of distribution network operators, charge station owners, and electric vehicle users. The problem is formulated by an uncertain mixed-integer programming model. Due to the multi-fold uncertainty of the charging process, the uncertain model parameters are expressed as Type-2 fuzzy variables (T2-FVs). The critical value-based type reduction method is adopted to handle the high computational complexity. The proposed uncertain model is converted to its equivalent deterministic chance-constrained programming model. The deterministic counterpart is solved by General Algebraic Modeling System (GAMS). At last, numerical simulations are performed to demonstrate the proposed location strategy as well as some sensitivity analyses. The results indicate that for any given parameters, the equivalent deterministic model follows the general form of mixed-integer programming one that can be easily solved by GAMS. The proposed methodology can effectively handle the multi-fold uncertainty of the charging process. Compared with crisp models, the proposed location strategy can provide more robust location decisions for electric vehicle charging stations (EVCSs). In addition, we also found that different interest groups have conflicting preferences for the locations of EVCSs, so considering multiple hybrid optimization preferences is necessary.

Benefits of Preservation, Green and Quality Improvement Investment for Waste Management in Sustainable Supply Chain under Fuzzy Learning and Inflation

In the modern world, waste management, incorporating the quality of the products, energy consumption, and environmental concern have become significant challenges for supply chain managers. Also, smart devices are essential for daily life in the current socioeconomic environment, and customers primarily contemplate a smart product’s price and energy usage before purchasing that. In this situation, to maintain a balance between the selling price, energy consumption, and carbon emission from supply chain operations becomes necessary. So this study develops a twoechelon sustainable inventory model for deteriorating items with an imperfect production process under energy consumption and selling price dependent demand. The producer makes a rework process and quality improvement investment to mitigate defective products and enhance the quality of the products. The present model develops under the influence of inflation. Also, preservation and green technologies are used to mitigate the rate of deterioration and carbon emission, respectively. Firstly, the model is created in a crisp sense, and then expanded into a fuzzy learning model to examine the impact of the learning effect in an imprecise environment. A numerical analysis is performed to validate the proposed model, and the cost function’s convexity is shown graphically using mathematica software. The result of the proposed model provides significant insights to decision-makers on how to efficiently reduce waste while still minimizing the total cost of the system by investing in high efficiency preservation, quality improvement and green techniques. Also, due to learning in fuzziness, the fuzzy learning model gives the lowest total cost, followed by the fuzzy and crisp model. Finally, for various parameters, a sensitivity analysis is performed to gather valuable observations and management insights.

An amalgamation of crisp and fuzzy quantile regression model

An amalgamation of crisp and fuzzy quantile regression model

Stability analysis of fuzzy differential equation for prey–predator system under Z-number fuzzy and granular differentiability concept

The stability analysis is one of the basic problems in the field of system, prey–predator, production inventory control, signal processing etc. The goal of stability analysis for prey–predator and production inventory control model is to determine the region of the different variables and parameters at which the system is stable or unstable. This paper explores an uncertain prey–predator model where both prey and predator populations are fuzzy variables. Also we have considered all the prey–predator parameters of the system are imprecise. This fuzzy prey–predator model is converted to an equivalent crisp model using granular differentiability approach. The condition of local stability is obtained mathematically by analysis the eigenvalues of characteristic equation. And also we introduced a production inventory model as a case study of fuzzy dynamical system to illustrate the efficiency of the result and practical aspects.

A non-linear mathematical approach for solving matrix games with picture fuzzy payoffs with application to cyberterrorism attacks

Numerous expansions of fuzzy sets have been proposed to manage uncertain information in real-world problems. Unlike fuzzy sets and intuitionistic fuzzy sets, picture fuzzy sets independently consider membership, non-membership, and indeterminacy. They have been extensively employed to delineate decision-makers’ perspectives when knowledge is lacking and have found application in numerous domains. However, little research has been conducted on uncertain matrix games in picture-fuzzy environments. This paper proposes an approach to resolving a matrix game with picture-fuzzy payoffs. Two non-linear mathematical programming models equipped with multi-objective functions are formulated and converted into crisp linear programming models by applying the weighted average approach. The reduced problems are solved using the LINGO platform, yielding optimal solutions. An illustrative example demonstrates how an information security agency can select the most effective strategies to combat cyberterrorism. Finally, comparing the presented methodology’s outcomes and existing work establishes its dependability and efficacy.

An optimization model for sustainable multi-product multi-echelon supply chain networks with U-shaped assembly line balancing under uncertainty

This study presents an integrated supply chain network with suppliers, manufacturers, assemblers, and customers. The proposed model considers a U-shaped assembly line with three sustainability objective functions. We consider assumptions considering different types of raw materials, multiple products, location selection of manufacturers, location selection of assemblers, and capacity of suppliers. The problem is formulated non-linear and then linearized as a multi-objective model. Some cost and demand parameters are considered uncertain and are represented by fuzzy sets and theory. The proposed uncertain model is first converted to a multi-objective crisp model by applying the modified robust possibilistic programming approach. Then, the obtained crisp multi-objective model is solved by an interactive-fuzzy optimization approach in the literature. For computational study, some test problems are generated and solved using an original deterministic formulation and the crisp form of the uncertain formulation. The obtained results are analyzed and compared according to the objective function values. Finally, an extensive sensitivity analysis is performed on the parameters of the models.

An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction

Predicting the prices of cryptocurrencies is more complicated than that of classical financial assets because they do not seem to have reached the maturity stage of their life. In addition, many known and unknown factors may affect Bitcoin prices; these factors and their importance seem to be changing faster than other financial assets. Therefore, the data used to predict the prices of cryptocurrencies can be considered big data challenging to manage due to their volume, variety, and variability. This study presents an integrated approach to managing the data when predicting the Bitcoin price. We first prepare a list of factors affecting the Bitcoin price. We then use the Fuzzy Analytic Network Process (FANP) to screen these factors and select the most important ones based on the experts’ opinions. The selected factors are considered independent variables affecting the Bitcoin price. Next, we extract a fuzzy regression model using the historical data in which the Bitcoin price is considered the dependent variable. Finally, this model is validated with different confidence levels, and the appropriate level is selected to predict the Bitcoin price. The results show that Bitcoin prices fall within the forecasting intervals obtained from the fuzzy regression model for a 99% confidence level. Unlike crisp regression models, the fuzzy regression model used in this study does not predict the Bitcoin price as a crisp value; instead, it predicts the price as an interval value. The contributions of this study are fourfold: (1) identifying the factors affecting the Bitcoin price and investigating their mutual impacts on each other; (2) determining the most influential factors using the FANP method; (3) using fear and greed as essential sentimental independent variables in regression to predict the Bitcoin price; (4) and predicting the Bitcoin price as an interval instead of a crisp value.

Preface

It is a great pleasure for us to welcome you here to Changsha for 2023 2nd International Conference on Power System and Energy Technology (ICPSET 2023), which was held in Changsha, China from 18th to 20th August 2023 in virtual form. About 80 delegates took part in the Conference.ICPSET 2023 provided an opportunity to present and discuss the latest news accumulated during last year. Another important feature of this series of meeting has always been the participation of a large number of related scientists and researchers from all over the world. It thus represented an important addition to the series of large international conference on power system and energy technology held every year in different regions of China.The ICPSET meeting brought together experts and scholars interested in studying power system and energy technology to an international platform for presenting and discussing recent results and achievements, including new experimental and theoretical methods in the fields. The meeting’s topics can be characterized as follows: Power System Real-Time Simulation System, Power Transmission, Energy Efficiency, Energy Saving Technology, New Energy Vehicle, etc.Since 2022, the series meeting of ICPSET has been held in China and has become a regular academic meeting investigating latest achievements on power system and energy technology. Recent years have seen a lot of new research and experimental results in related fields. Many excellent papers of plenary lectures and contributions presented at ICPSET 2023 were published in the conference proceedings by Journal of Physics: Conference Series.We listened to the reports of experts from leading universities and research organizations in related fields. Among the many invited speakers, Prof. Yicheng Zhou from Waseda University, Japan delivered a keynote speech on Grid Forming Inverters: Current Status and Some Challenges Issues for the Future, Prof. Denis Sidorov from Siberian Branch of Russian Academy of Sciences and The Skolkovo Institute of Science and Technology, Russia on Crisp and Fuzzy Models for Net-zero Communities’ Development, Prof. Jianbo Bai from Hohai University, China on Development and Application of Digital Technology for Distributed PV Power Stations Under Full Life Cycle, and Prof. Yong Li from Hunan University, China on Autonomous Operation and Energy Management of Multi-Energy Microgrid.We are grateful to all the people who were involved in the whole process of the Conference. We gratefully acknowledge the support from Hunan University (China), as well as the contributions from each author. We also appreciate the publishing support from relevant staff of Journal of Physics: Conference Series.The Committee of ICPSET 2023List of Committee Member is available in this Pdf.

A novel technique for solving intuitionistic fuzzy DEA model: an application in Indian agriculture sector

Fuzzy DEA is a performance measurement tool that is used to assess the performance of DMUs in highly uncertain environments. In this article, the Intuitionistic fuzzy DEA (IFDEA) model is proposed based on the triangular intuitionistic fuzzy numbers (TIFNs). The weighted Possibility mean for TIFN is used to compare and rank the TIFN. The weighted possibility mean approach is proposed to solve the IFDEA model, and the IFDEA model is converted into its equivalent crisp DEA model to assess the relative efficiencies of the DMUs. One advantage of the proposed approach is that the attitude of the decision-maker is considered while measuring the efficiency of the DMUs. The weight or risk factor delta in [0,1] indicates whether the decision-maker is a risk-taker, neutral, or adverse. The crisp DEA model is a LP problem that is solved by using an existing LP method with different risk factors to determine the efficiency score of the DMUs. The DMUs are ranked based on the overall efficiency score of the DMUs, which is the arithmetic mean of the efficiency scores of the DMUs with different risk factors. Two numerical examples are given here to demonstrate the validity and applicability of the proposed technique and to compare the performance of the DMUs in the proposed approach with the exciting ranking approach and the expected value approach. A case study on the agriculture sector has been conducted in order to evaluate the agricultural performance of Indian states using the IFDEA model. According to the results of the IFDEA model, 15 (53.57%) out of the 28 Indian states were found to be efficient.

Fuzzy inference system model for the spectrum characteristic periods of the Türkiye Building Earthquake Code (2007)

Seismic design codes define response spectra with crisp numerical classifications of seismic parameters, which mainly affect the spectrum's shape and determination of seismic design loads. The efficiency of structural safety and construction costs depends on the optimum design and accurately determined seismic forces. As presented in the seismic design codes, several parameters are utilized to calculate the seismic design forces with response spectra. This study proposes a rule-based fuzzy inference system (FIS) model with fuzzy set numbers to determine the relevant parameters. By defining the soil profile thickness and shear wave velocity as inputs, the model generates the spectrum characteristic periods specified in the Türkiye Building Earthquake Code (TBEC 2007). The response spectra of twenty different samples with the FIS and crisp models were generated and compared to assess the model's superiority. Unlike crisp seismic code classifications, the proposed FIS model accounts for imperfections in soil group selection and topmost soil layer thickness, offering a more realistic representation of uncertainties and proving to be an effective tool for addressing linguistic vagueness in seismic response spectra analysis. The comparison between fuzzy and crisp output seismic parameters revealed significant differences in response spectra shape and spectrum intensity values. The FIS model-generated spectra were more conservative in certain building locations, while in others, they provided similar or lower values, suggesting potential cost savings in design. The FIS model demonstrates its efficacy in producing more accurate and robust designs by considering the uncertainties inherent in the problem. Furthermore, this approach has the potential to be extended to study seismic parameters of other design codes, although further research is required to comprehensively explore its capabilities and limitations.

Fuzzy inference system model for the spectrum site coefficients of the Türkiye Building Earthquake Code (2018)

The response spectra in the seismic design codes guide the seismic design force calculations that are crucial for structural safety and construction costs. In general, they are determined by crisp logic-based numerical classifications of seismic parameters, which primarily affect the shape and values of the spectrum. Crisp logic response spectra generation may have limitations in the accurate determination of relevant parameters. In this paper, a fuzzy inference system (FIS) rule-based model is proposed to address this issue. The proposed model uses spectral acceleration coefficients and shear wave velocity as inputs to generate the site coefficients of the response spectra according to the Türkiye Building Earthquake Code (TBEC 2018). The FIS model spectra generation is compared with the crisp logic model for thirty-five examples, and the model performance is evaluated. The study demonstrates significant differences between the two models in terms of the response spectra' shape and acceleration spectrum intensity values, but the FIS model provides comparatively better accuracy in response spectra generation. The proposed model can be modified and applied to various parameters for generating response spectra in different seismic design codes with the exposition of fuzzy logic rules that show the concerned problem's internal working mechanism.

A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization

This paper is based on some assumptions validated using real data and the most used Forex trading platform Meta Trader 4. First, we assume that any reasonable and relatively simple models, reflecting some trading hypotheses, can be profitable for a certain period. Having a sufficient set of such models optimized for selected currency pairs, we can use the model that provides the greatest profit in the current trading period. The second proposal is to use a fuzzy multiple-criteria approach at the training optimization stage using historical data in order to overcome or reduce the negative effect of overfitting. Here for the first time, the problem of fuzzy multiple-criteria optimization of trading was formulated and solved based on the output parameters of the developed simple single-criteria crisp models. This provides significantly greater profit than that obtained using single-criteria crisp models. The third proposal is to use the hierarchical structure of fuzzy local criteria to solve the multiple-criteria problem. It is shown that this additionally provides a significant increase in profit. The profitability and riskless of the developed trading models are studied based on real quotations of currency pairs USDJPY, EURUSD and AUDUSD using H4 timeframe.