Fuzzy Optimization and Modeling Journal (FOMJ)
Fuzzy Optimization and Modeling Journal (FOMJ), a peer-reviewed research and open-access journal, has four annual issues. It provides a global forum for exchanging research findings and case studies that bridge the latest fuzzy technology and various optimization techniques. It promotes how fuzzy technology improves decision techniques and how the development of optimization methods affects fuzzy technology. This journal is peer-reviewed and publishes high-quality theoretical and practical papers on a broad range of fuzzy optimization-related topics. Journal DOI: 10.57647/fomj Journal Abbreviation: Fuzzy Optim. Model. J. Journal Title Acronym: FOMJ Journal Owner: Islamic Azad University Publisher: Islamic Azad University, Co-publishing, OICC Press, UK Keywords: Fuzzy Sets, Fuzzy Systems, Fuzzy Optimization , Fuzzy Modeling , Soft Computing , Artificial Intelligence Editor-in-Chief: Professor Ali Ebrahimnejad, PhD ( 0000-0001-6003-6601)
DFT-Based fuzzy topological indices integrated with machine learning for accurate prediction of topical drug properties
In this study, we developed a novel framework to predict the physicochemical properties of topical drugs by integrating fuzzy topological indices with machine learning (ML) models. The chemical structures of the selected drugs were optimized using Gaussian software, and both bond lengths (edges) and vertex properties, corresponding to the atomic masses of each molecule, were fuzzified through Density Functional Theory (DFT). Fuzzy topological indices (FTIs) were then calculated to capture the relationships between molecular geometry, atomic composition, and topological features. Two machine learning algorithms, Linear Regression (LR) and optimized Support Vector Regression (SVR-Tuned), were employed for property prediction. The models were trained on the main dataset and validated on additional test drugs excluded from training, enabling a rigorous assessment of generalization, predictive accuracy, and the absence of overfitting. The results showed that the proposed fuzzy QSPR framework, combined with optimized, achieves high predictive performance, robustness, and strong generalization. This methodology provides an efficient computational tool for estimating molecular properties and can support the rational design of next-generation topical pharmaceutical agents.
Copyright (c) 2025 Negar Kheirkhahan, Masoud Ghods (Author) https://creativecommons.org/licenses/by/4.0
Vague Graph Structures: Some New Concepts and Applications
A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we apply the concept of vague sets to graph structures. We introduce certain notions, including vague graph structure (VGS), strong vague graph structure, vague B<!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1827661754"> </o:OLEObject> </xml><![endif]-->-cycle, and illustrate these notions by several examples. We study P<!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1026" DrawAspect="Content" ObjectID="_1827661755"> </o:OLEObject> </xml><![endif]-->-complement, self-complement, strong self- complement, totally strong self-complement in vague graph structures, and investigate some of their properties. Finally, an application of vague influence graph structure is given.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
A Framework for Identifying and Analyzing Drivers Affecting the Futures of Cryptocurrency FinTechs in Iran with Fuzzy Delphi and Fuzzy Dematel
The present research aimed to identify and analyze effective drivers in the future of crypto fintechs using a fuzzy approach. The research was applied in terms of purpose and had a quantitative methodology. The theoretical population consisted of financial experts in fintechs and block chain technology. Judgmental sampling was performed based on the expert's expertise. A sample size of 10 was studied. Two quantitative techniques, fuzzy Delphi and DEMATEL, were utilized in the study, and the expert and effect assessment questionnaires, which had desired validity and reliability, were used to collect data. The research had three stages; first, 22 drivers were obtained using the literature review and interviews with experts. Second, the drivers were screened by distributing expert assessment questionnaires and the fuzzy Delphi method. Third, 10 drivers were selected for the final analysis due to the suitable deffuzification number. Such drivers were evaluated by distributing effect assessment questionnaires and the fuzzy DEMATEL technique. The drivers of national regulatory policies, the development of national RegTechs, and the development of smart contracts in the financial industry respectively had the highest net effects. Practical suggestions were extracted based on prior drivers and interviews with focus groups.
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0
Fixed Point Theorem in Fuzzy Metric Space
In this present work, we prove fixed point theorem for contractive mapping F: X--X in fuzzy metric spaces that have a nonempty F− invariant complete subspace E, then prove the uniqueness the fixed point in E. Though many theorems in fuzzy metric space in this case, our theorem is a new type of these theorems as it is we proved that a unique fixed point there exists in F− invariant complete subset E in X. Finally, we give an interesting example in complete fuzzy metric space that satisfies in the conditions of our theorem and prove the uniqueness fixed point of F.
Anti-Fuzzy Bi-Ideals in Semirings Under S-norms
In this paper‎, ‎by using S-norms‎, ‎the notion of anti fuzzy bi-ideals in semirings will be defined and investigated some properties of them‎. ‎Next prime‎, ‎strongly prime‎, ‎semiprime‎, ‎irreducible‎, ‎strongly irreducible of them will be introduced and obtained some results about them‎. ‎Latter‎, ‎they will be investigated under regular and intra-regular semirings‎. ‎Finally‎, ‎they will be characterized under totally ordered by inclusion‎.
Choosing the Best Company for Investment according to the Financial Factors in the Neutrosophic Environment (Case Study: Automotive Industry)
The analysis of financial ratios is one of the methods of examining the state of companies, which expresses the relationships between the items in the financial statements. The study of financial ratios helps us to get a comprehensive and correct understanding of the performance and financial status of companies and to benefit from them when investing and checking the efficiency of companies. Since many current phenomena in finance and the economy are ambiguous, they are treated as if they are clear, so this article, using Neutrosophic, examines the selection of a company for investment using financial ratios in the Neutrosophic environment. Where verbal judgments (qualitatively) are used for decision-making, the proposed approach will perform better than previous approaches. To rank the options in this research, eight financial ratios widely used in investment analysis are considered indicators of the problem. Then, ranking the alternatives is done by defining the ideal option (with the best performance in all indicators) and applying the known criterion of similarity. A numerical example is operated to understand the proposed approach better, and the results are presented.
Coping Uncertainty in the Supplier Selection Problem Using a Scenario- Based Approach and Distance Measure on Type-2 Intuitionistic Fuzzy Sets
Supplier selection (SS) is a process in which companies identify, evaluate, and select suppliers. The MCDM methods are often used for supplier selection in supply chain management. An unlimited number of MCDM techniques, such as analytic hierarchy process (AHP), analytic network process (ANP), the technique of order preference distance to the ideal solution (TOPSIS), etc., have been deployed to solve the supplier selection problems. Though they can manage problem complexity, MCDM techniques cannot deal with problem uncertainty. Hence, they have been combined with the fuzzy set, intuitionistic fuzzy set, etc., to provide more accurate solutions to supplier selection problems. Nonetheless, the future uncertainty related to the environmental changes is ignored in the SS literature. Therefore, we use future scenarios as criteria to select the best supplier in this study. Moreover, we applied a distance measure to rank Type-2 Intuitionistic Fuzzy Set, which is a suitable approach to deal with the vagueness of verbal judgments. A numerical example explains the 5-step proposed approach.
Fuzzy-enhanced hybrid recommender system using autoencoder and GRU with fuzzy ACO weighted fusion
Personalization in large-scale systems is made possible by recommender systems. Collaborative filtering is a traditional approach for recommendation, but it struggles to handle inaccuracy, data sparsity, and prediction error. Our study proposes a hybrid recommendation model that integrates timestamps, user characteristics, and item characteristics to enhance prediction evaluation criteria, thereby addressing these limitations. A new user feature was created by fuzzy c-means clustering algorithm. Using three separate learning mechanisms, AutoEncoder (AE) and Singular Value Decomposition (SVD), the method produces initial rating predictions based on the user features and item features, which are combined with user-item ratings. The Gated Recurrent Unit (GRU) model takes a combination of user-item ratings and their timestamps as input to identify temporal patterns in behaviors and make another initial prediction. The independent predictions are combined by an optimization process, which is then followed by a weighted averaging step. The Fuzzy Ant Colony Optimization (Fuzzy ACO) method is utilized at this point to fine-tune the weights to carry out the ultimate prediction. The efficacy of our hybrid model was evaluated on the MovieLens 1M and MovieLens 100K datasets. In terms of RMSE, MAE, precision, and recall, our model surpasses baseline models in evaluation measures.
Keywords Recommender system, Fuzzy c-means, AutoEncoder, Fuzzy ACO, GRU
Copyright (c) 2025 Mahshid Salehi, Hassan Shakeri, Seyyed Javad Seyyed Mahdavi Chabok (Author) https://creativecommons.org/licenses/by/4.0
A hybrid fuzzy cognitive–system dynamics framework for modeling environmental risk evolution in construction megaprojects
Emphasizing the critical role of sustainability, this research comprehensively assesses environmental risks in industrial-construction megaprojects and shows that identifying and managing these risks can help reduce negative environmental consequences and improve sustainable decision-making. The present study aims to analyze the environmental risk management system in a megaproject as a case. Therefore, the innovation of this research lies in the use of a system dynamics approach and fuzzy cognitive mapping to identify and assess environmental risks in large-scale industrial and civil engineering projects. Based on this analysis, each aspect of a project's environmental risk management is ranked using the Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) and causal relationships are examined. Also, using the Fuzzy Cognitive Map (FCM) approach, the in degree, out degree, and centrality of each risk have been identified. Then, the behavioral model is determined based on the system dynamics. The risk of completing the project timely and the issues related to the increase in time due to environmental laws were ranked first, an over predicted increase in the project time due to environmental laws was ranked second, and the factor of environmental issues and problems was ranked third. Environmental issues and problems have the highest impact and the risk of completing the project timely and the issues related to the increase in time due to environmental laws were the most affected factor. Experts believe that the involvement of several institutions and decision-making from the perspective of environmental climate issues is one of the most significant environmental risks affecting the completion of the project. This issue is because the risk of completing the project timely from an environmental perspective and environmental issues and problems is reduced by controlling and reducing this risk. These two risks are increasing with an increase in the risk of conflict among several decision-making institutions from the perspective of environmental climate issues. The risk of the involvement of several decision-making institutions from a climate and environmental perspective is directly associated with two risks of environmental issues and problems and the risk of completing the project timely. Thus, the institutions involved in the climate and environmental issues of the project can reduce the risk of timely completion of the project and environmental issues and problems with mechanisms and memorandums that reduce the risk of conflict among them.
Copyright (c) 2025 Seyed Amin Seyedi, Alireza Mirjalili, Mahoudreza Maheri, Abolfazl Sadeghian (Author) https://creativecommons.org/licenses/by/4.0
Two novel distance functions for interval-valued trapezoidal intuitionistic fuzzy numbers and their applications in decision-making
Despite the extensive application of interval-valued intuitionistic fuzzy numbers (IVIFNs) in modeling uncertainty within complex decision-making problems, distance measurement for their important subclass, interval-valued trapezoidal intuitionistic fuzzy numbers (IVTraIFNs), suffers from conceptual and computational limitations. Existing distance measures often rely on discrete features or neglect the continuous geometry and surface structure of these numbers, leading to information loss and inadequate discrimination between alternatives. This paper addresses this gap by introducing a novel surface-area-based distance measurement framework designed explicitly for IVTraIFNs. We propose two new distance functions, a Euclidean and a Manhattan measure, derived from the integration of areas under the left and right curves of the continuous membership and non-membership functions. A conceptual parametric framework for representing continuous IVTraIFNs is first established, and the mathematical properties of the proposed measures, including compliance with distance metric axioms and the triangle inequality, are formally proven. Explicit, practical formulas for these measures for IVTraIFNs are then developed. To demonstrate practical efficacy, the new measures are systematically integrated into two established multi-criteria group decision-making (MCGDM) methods: TOPSIS and CODAS. A comprehensive numerical example in the context of investment project selection, supported by sensitivity analysis on the weight parameter and standard validity tests, confirms the effectiveness, robustness, and advantages of the proposed approach compared to some existing methods. The results indicate that the surface-based distance measures provide more appropriate discrimination between alternatives while offering flexibility through a parameter that weights the membership and non-membership components, remaining consistent with existing decision-making schemes.
Interval-valued intuitionistic fuzzy set, Interval-valued trapezoidal intuitionistic fuzzy number, Distance measure, Multi-criteria group decision-making.
Copyright (c) 2025 Madineh Farnam, Majid Darehmiraki (Author) https://creativecommons.org/licenses/by/4.0
A fuzzy-based planning method and meta-heuristic optimization for traffic signal control
To regulate traffic volume and average vehicle delay, traffic signal control systems aim to develop a computational model to examine vehicle lines on urban roadways. Based on the optimization of vehicle speed on each street and traffic light timing, a hybrid traffic signal control system based on fuzzy logic and wild horse optimization algorithm (HFWHO-TSC) is introduced in this study. The first phase is optimized using the wild horse meta-heuristic algorithm (WHO) technique, and the timing of the traffic lights is optimized using fuzzy logic depending on the traffic intensity of the nearby streets in various directions of the intersection. Additionally, an iterative loop is used in this work to find the ideal fuzzy time for traffic light lighting. The proposed hybrid system is compared with two other controller systems: a fixed-time signal controller and a signal controller that combines particle swarm optimization algorithms and fuzzy logic in a hybrid manner (HFPSO-TSC). With a performance gain of almost 37% over the fixed-time traffic controller and 20% over HFPSO-TSO, respectively, the HFWHO-TSC solution outperformed the others. The proposed hybrid system's performance effectively reduces both traffic congestion and driver annoyance brought on by signals. Additionally, it lessens fuel consumption and pollution.
Traffic Signal Control (TSC) system; Fuzzy logic system; Wild Horse Optimization (WHO) algorithm; Particle Swarm Optimization (PSO) algorithm; Matlab software; Fuzzy time.
Copyright (c) 2025 Seyed Mehdi Seifivand, Parvaneh Asghari, Hamid Haj Seyyed Javadi, Hamzehali Nourmohammadi (Author) https://creativecommons.org/licenses/by/4.0
A new weighted goal programming technique to solve fully triangular intuitionistic fuzzy multi-objective fixed charge solid transportation problem
Modern society depends substantially on transport because it allows for mobility and the movement of goods. However, it also has numerous negative effects on the environment and makes a major contribution to carbon emissions. Different vehicles use a variety of fuels to transmit energy in transportation systems. For energy communication, vehicles emit greenhouse gases that pollute the environment. Also, the parameters in transportation problems are not always precisely define rather are imprecise in nature in most cases. Recalling this fact, this paper explores the study of a multi-objective fully intuitionistic fuzzy fixed charge solid transportation problem considering carbon emission as an objective function. A new solution algorithm is developed to obtain the compromised optimal solution of the proposed model. Solved the proposed transportation problem models by using LINGO.19.0 optimization software and obtained the crisp as well as fuzzy objective values. A real-life-based numerical problem is presented and solved to validate the concept and the results are compared with other existing methods.
Copyright (c) 2025 Awdhesh Kumar Bind, Deepika Rani, Kapil Kumar Goyal (Author) https://creativecommons.org/licenses/by/4.0
Simultaneous Minimization of Cost, Cycle Time, and Workstations in Intuitionistic Fuzzy Assembly Line Systems
Assembly line design is done to coordinate a set of sequential activity stations that are used to maximize the utilization of workstations and manpower. The stations are deployed in such a way that materials flow through them continuously, continuously and at a constant rate. In this paper, a multi-objective model in intuitionistic fuzzy conditions is proposed for the assembly line balance problem. In order to solve the proposed model, a two-stage method is presented. In the first stage, the multi-objective problem of assembly line balance in intuitionistic fuzzy conditions is transformed into a deterministic multi-objective problem, and in the second stage, an efficiency solution for the multi-objective problem is obtained using the fuzzy programming method. In order to demonstrate the efficiency of the proposed method, a numerical example is presented.
Multi-objective assembly line balancing problem; Trapezoidal intuitionistic fuzzy numbers; Fuzzy mathematical programming; Pareto solution.
Copyright (c) 2025 Ali Mahmoodirad (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Image-Based Recognition of Rice Cultivars Using PSO-Optimized ANFIS
Rice is considered to be one of the most significant staple foods on a global scale, particularly within developing countries such as Iran. In these countries, rice cultivars frequently exhibit considerable variation in terms of quality, price, and characteristics. Accurate identification of rice cultivars is imperative for maintaining market transparency, ensuring quality control, and supporting agricultural decision-making. This study proposes a novel methodology for the classification of rice cultivars, which integrates advanced image processing techniques with an Adaptive Neuro-Fuzzy Inference System (ANFIS) that has been optimised by the Particle Swarm Optimisation (PSO) algorithm. A total of three rice types, which are commonly cultivated in northern Iran, were selected for the study: Native Tarom, Hashemi Tarom, and Pakistani rice. The images from these three rice types were processed in standard conditions using Canny edge detection algorithms. The extraction of morphological features was then utilised for the training and testing of the ANFIS-PSO model. The experimental results obtained achieved a classification accuracy of 99.47%, thereby demonstrating the superiority of the proposed method in comparison to traditional techniques with regard to precision and applicability. Moreover, the method employs a cost-effective and readily accessible approach that lends itself to practical applications, including the development of smartphone-based software for real-time rice identification. This study proposes a non-invasive, efficient, and scalable solution to rice authentication and classification challenges, with potential application in agricultural quality assurance and market regulation.
Copyright (c) 2025 Sayedeh Fatemeh Sakhaei, Ahmad J. Afshari, Alireza Bosaghzade, Meghdad H.M. Jahromi (Author) https://creativecommons.org/licenses/by/4.0
A new approach to managing maintenance and repairs of offshore oil and gas platforms using fuzzy-DEMATEL
The maintenance and repair of offshore oil and gas platforms are of critical importance in ensuring operational efficiency, safety, and environmental protection. Conventional maintenance strategies frequently encounter difficulties in addressing the intricacies and uncertainties intrinsic to offshore environments, resulting in elevated downtime and augmented operational expenditures. This study proposes a novel approach that integrates fuzzy logic with the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method, termed fuzzy-DEMATEL, with a view to enhancing maintenance management in offshore settings. The proposed methodology employs fuzzy logic to effectively capture the uncertainties associated with various maintenance factors, while DEMATEL provides a systematic framework for analyzing and visualizing the relationships between these factors. The objective of this research is to develop a comprehensive maintenance management framework that prioritizes maintenance activities based on a holistic assessment of equipment reliability, environmental conditions, and operational constraints. A comprehensive review of the extant literature and a series of case studies were undertaken in order to evaluate the effectiveness of the fuzzy-DEMATEL approach. The results obtained demonstrate the potential of this approach to improve decision-making processes and optimize maintenance strategies in offshore oil and gas platforms. The results obtained in this study indicate that intelligence (C10) has the highest degree of influence, while maintenance and repair management (C1) exhibit a very high level of influence. Furthermore, (C1) exhibits the highest degree of interaction with other criteria. The findings of this study contribute to the advancement of maintenance management practices in the industry, paving the way for more sustainable and efficient operations.
Maintenance and repair management, offshore platforms, oil and gas, fuzzy-DEMATEL approach
Copyright (c) 2025 Reza Dehghani, Abolfazl Sadeghian, Hassan Dehghan Dehnavi, Mozhde Rabbani, Mohammad Taghi Honari Honari (Author) https://creativecommons.org/licenses/by/4.0
Predicting Efficiency Scores for Digital Transformation Management in the Banking System under Uncertainty Using Stochastic Data Envelopment Analysis and Machine Learning
Digital transformation in the banking system is presented as a comprehensive and multi-dimensional process influenced by various factors, such as customer experience, operational efficiency, innovation adoption, and risk management. These factors are recognized as the main criteria for evaluating and managing digital transformation. The present study analyzes the effectiveness of digital transformation in banking using the network data envelopment analysis method and examines the sensitivity of banks' efficiency scores to data changes. The results show that banks with higher efficiency scores and lower sensitivity are more successful in their digital transformation efforts. Additionally, neural network machine learning techniques have been used to identify hidden patterns and predict data. The presented model has predicted the data with high accuracy and has been able to explain nine key components of digital transformation.
Digital Transformation Management, Efficiency, Data Envelopment Analysis, Machine Learning
Copyright (c) 2025 Mohammad Shirkhoda, Ladan Riazi, Faramarz Fathnejad (Author) https://creativecommons.org/licenses/by/4.0
Identifying and prioritizing obstacles to the implementation of building information modeling in sustainable construction projects in Iran using hybrid Fuzzy Delphi-BWM approach
With the growing global adoption of Building Information Modeling (BIM) technology in the construction industry, this innovative approach has also gained momentum in countries like Iran. However, its practical implementation continues to face numerous challenges. To support broader adoption-particularly within the context of sustainable construction projects, which emphasize environmental, economic, and social considerations throughout the project lifecycle-it is crucial to identify and address these barriers. This study aims to identify and prioritize the key obstacles to BIM implementation in sustainable construction projects in Iran. An initial list of challenges was developed based on a comprehensive literature review and validated through the Fuzzy Delphi Method (FDM), which resulted in the identification of 31 relevant obstacles. Among these, ten were selected as the most critical for further analysis. The Best-Worst Method (BWM) was then applied to rank these barriers. Pairwise comparisons between the best and worst criteria were conducted, and the resulting optimization model was solved using Lingo 18 software. The findings reveal that the most significant challenges are: (1) the absence of clearly defined processes for applying BIM in sustainable building projects (weight = 0.12), (2) the lack of standardized guidelines for BIM integration (weight = 0.107), and (3) the absence of a legal framework for BIM implementation (weight = 0.106). These insights can inform policymakers and stakeholders seeking to improve BIM adoption in the context of sustainable development.
obstacles and challenges of building information modeling
Copyright (c) 2025 Sarina Alizadeh Fard, Mohammad Rostami, Mohammad Mahdi Shahbazi (Author) https://creativecommons.org/licenses/by/4.0
A robust model for the Best-Worst Method (BWM) based on goal programming
The utilization of effective decision-making methodologies has historically captured the attention of decision-makers. The Best-Worst Method (BWM) is a prominent and extensively utilized technique in Multi Attribute Decision Making (MADM), owing to its simplicity, reduced number of pairwise comparisons, and high consistency of results. However, the original BWM operates under the assumption that decision-makers' judgments are precise and certain. In real-world conditions, uncertainty is prevalent. Furthermore, when addressing MADM problems that are not fully consistent and involve more than three criteria, the BWM technique may not result in a unique optimal solution. To address these limitations, this study proposes a Robust BWM approach based on goal programming and formulated as a linear optimization model. This approach incorporates interval preferences to model uncertainty in judgments. Additionally, a methodology is put forward to assess the Consistency Ratio (CR) of input data in the presence of uncertainty, thereby ensuring the reliability of the results obtained. The performance of the model has been examined through six numerical examples, and its validity has been evaluated. The findings suggest that the proposed method exhibits adequate efficiency and performance in addressing MADM problems and managing uncertain data. Consequently, the proposed method can serve as an effective solution for decision-making problems in real-world and uncertainty-based environments.
Copyright (c) 2025 Mohammad Reza Dehghani, Dr. Ramin Sadeghian, Dr. Meisam Jafari Eskandari (Author) https://creativecommons.org/licenses/by/4.0
Fuzzy logic approach in the evaluation of radiation shielding behaviour of borided Co-Cr-Mo alloy
The present study investigates the radiation shielding behaviour of borided Co-Cr-Mo alloys. The investigation is conducted by utilising a fuzzy logic model, which is employed to analyse the relationship between the boriding temperature and boron content of the boriding powder. In order to achieve this objective, the linear attenuation coefficients of borided alloys were measured as a function of boriding temperature and boron content. Subsequently, a fuzzy logic model was applied to these measured values. The findings demonstrated that the outcomes derived from the measured and fuzzy logic methods exhibited strong concordance. The linear attenuation coefficient is contingent upon the boriding temperature and the boron content. Furthermore, the variation of the linear attenuation coefficient with boriding temperature is more significant than its variation with the boron content. It was concluded that the fuzzy logic model can be used to predict the radiation shielding behaviour of borided Co-Cr-Mo alloys without the need for further experimentation, even under conditions that have not yet been tested. The present study will contribute to the selection and development of radiation shielding materials by demonstrating practical applications of fuzzy logic.
Copyright (c) 2025 Ali Osman Ucar (Author) https://creativecommons.org/licenses/by/4.0
Development of a series-parallel data envelopment analysis model within an intuitionistic fuzzy environment
Data Envelopment Analysis (DEA) is a widely used tool for evaluating the efficiency of Decision-Making Units. However, traditional DEA models often encounter limitations when applied to complex, multi-stage systems, particularly in the presence of uncertainty and ambiguity in the data. To address these challenges, this study introduces a series–parallel DEA model that integrates both optimistic and pessimistic evaluations within an intuitionistic fuzzy environment. The model is specifically designed to provide a more accurate and nuanced assessment of multi-stage system efficiency, especially when handling diverse data types commonly encountered in real-world settings, including crisp, fuzzy, and intuitionistic fuzzy data. Two numerical examples are presented to validate the proposed model: one based on synthetic data and another adapted from real-world data reported by Kao. These case studies illustrate the model’s effectiveness and practical applicability in evaluating the performance of multi-stage systems with complex and uncertain data. By incorporating both optimistic and pessimistic perspectives, the proposed framework offers a comprehensive and balanced evaluation of DMU performance, making it a valuable tool for decision-makers operating under uncertain and dynamic conditions.
Copyright (c) 2025 Hoda Moradi, Hamid Babaei Meybodi (Author) https://creativecommons.org/licenses/by/4.0
A hybrid fuzzy framework for clustering and ranking listed companies via the K-means algorithm
The extant literature indicates that companies, including those listed on the Tehran Stock Exchange, are typically classified based on their respective industries. The findings of this study indicate that a reclassification of these companies may be warranted if alternative indicators are to be considered in the clustering process. To this end, the present study employs six indicators for the purpose of clustering the companies: price-to-return ratio (P/E), volume, liquidity rating, return on equity (ROE), β coefficient, and return on capital. The values of these indices are extracted for each company, and then the companies are categorized into eight clusters using the K-means method. The significance of these groups varies from user to user, contingent upon the relative importance of the indicators from the user’s perspective. To categorize these clusters, we initially employ the Fuzzy Analytical Hierarchy Process (FAHP) to ascertain the weights of the indexes. Subsequently, we utilize the TOPSIS method to prioritize the clusters. The results indicated that, according to the experts’ assessments, the P/E ratio, beta coefficient, and liquidity rating, with weights of 0.365, 0.221, and 0.212, respectively, are the most significant indicators for the evaluation of the clusters. It is imperative to acknowledge that the decision matrix employed in this study is predicated on the assumption that the centroid of each cluster serves as its representative. The approach delineated in this study has the potential to serve as a foundational element in subsequent research endeavors focused on portfolio construction based on firm similarity.
Copyright (c) 2025 Hamidreza Rezaei, Abdollah Hadi-Vencheh, Hosein Arman, Mehrdad Nikbakht (Author) https://creativecommons.org/licenses/by/4.0
A Fuzzy Logic-Based Framework for Multi-Objective Controller Placement in Software Defined Networks
Software Defined Networking (SDN) has been hailed as a revolutionary development in network management, with the potential to transform the landscape of the industry. The core principle behind SDN is the decoupling of the control plane from the data plane, a move that has been widely regarded as a significant step towards more centralized control. Nevertheless, determining the optimal placement of controllers, known as the Controller Placement Problem (CPP), remains a significant challenge due to conflicting objectives such as latency, resilience, and load balancing. This paper proposes a novel approach leveraging fuzzy logic to address the CPP. Distinct from heuristic or optimization algorithms, this approach employs fuzzy set theory to manage imprecise and dynamic network parameters while optimising multiple objectives, including latency, inter-controller communication, and load balancing. The proposed method provides a flexible, adaptable framework for CPP across varying network topologies. Experimental results validate the effectiveness of this approach in achieving balanced trade-offs among objectives, ensuring efficient and scalable SDN management, with a 18.7% reduction in maximum node-to-controller latency and a 22% improvement in load imbalance compared to state-of-the-art methods.
Controller Placement Problem Multi-Objective Optimization
Copyright (c) 2025 Ahmad Jalili (Author) https://creativecommons.org/licenses/by/4.0
Optimizing bipolar fuzzy fractional programmingproblems using the variable neighborhood searchalgorithm
In this paper, we propose a new variable neighbourhood search algorithm for solving a bipolar fuzzy number fractional problem (FBVNS). The proposed algorithm utilises a modified version of Kerre's inequality, a tool employed to compare bipolar fuzzy numbers. The utilisation of the FBVNS algorithm enables the direct resolution of the bipolar fuzzy number fractional problem, obviating the necessity for conversion to a crisp number representation. The FBVNS algorithm is further enhanced by the introduction of a novel local search method, which is informed by ascending directions derived from the resolution of four distinct crisp mathematical programming problems. Finally, the efficacy of the proposed algorithm is demonstrated through its implementation on a range of problems.
Copyright (c) 2025 Dhurgam Kalel Ibrahim Alsaad, Aliasghar Foroughi, Khatere Ghorbani-Moghadam, Reza Ghanbari (Author) https://creativecommons.org/licenses/by/4.0
A hybrid method for medical image denoising and segmentation using optimized fuzzy clustering and autoencoder
Frequently, medical images contain noise, which has the potential to obscure important diagnostic features and complicate accurate segmentation. The proposed framework integrates adaptive autoencoder-based enhancement, optimized fuzzy clustering, and fast bilateral filtering to achieve robust denoising and segmentation. In the preprocessing phase, fast bilateral filtering preserves edge details while reducing noise, thereby creating a reliable basis for subsequent clustering. In order to surmount the limitations of standard fuzzy C-means (FCM) in noisy conditions, the method employs the computation of the absolute difference between the original and filtered images. This process serves to accentuate structural information and suppress residual noise. Subsequently, an adaptive autoencoder is employed to reconstruct sharp, noise-free images by enhancing fine details. The efficacy of the clustering process is augmented by a refined FCM objective function that expedites convergence through a logarithmic accumulation of membership values from prior iterations. Finally, post-processing steps—including sharpening and median filtering-enhance the clarity and accuracy of segmentation results. The experimental findings, derived from the analysis of benchmark MRI and low-dose CT datasets, demonstrate the efficacy of the proposed method in surpassing classical FCM and other contemporary denoising techniques. The employment of this method engenders several key advantages, including enhanced noise reduction, expedited convergence, and refined segmentation accuracy. Furthermore, it guarantees the maintenance of essential diagnostic characteristics, thereby enhancing the reliability and utility of the resulting data.
Copyright (c) 2025 Omid Darvishi, Vafa Maihami, keyhan Khamfroosh (Author) https://creativecommons.org/licenses/by/4.0
Assessing Relationships in Industry and Optimizing Related Decisions with the Help of Fuzzy Properties
The textile industry supply chains (SC) face numerous risks and disruptions due to the changing dynamics of high demand and limited resources. In this context, the textile sector in these economies must prioritize Sustainable Supply Chain Management ‘(SSCM) to achieve cost reduction, enhance productivity, and improve profitability to sustain their business. Although research has examined several SSCM viewpoints, the barriers that prevent emerging economies from adopting SSCM in the textile sector to meet the Sustainable Development Goals (SDGs) are not sufficiently highlighted in the empirical literature that has already been published. This study analyzes different barriers and investigates how they are interconnected. From the literature research, 17 main barriers were first identified in the process. The barriers were then prioritized in order of significance using a combination of fuzzy theory, Pareto analysis, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) framework. Finally, the cause-and-effect relationships among these barriers were established. A lack of commitment from the supplier’s top management, insufficient financial incentives, and the absence of supportive government standards and regulations were identified as the three topmost significant barriers to SSCM’ adoption. For the textile sector, governments, and policymakers in emerging economies, the study’s results are helpful since they will assist them create mitigation strategies to get rid of these barriers and achieve long-term sustainability.
Copyright (c) -1 Mehdi Fazli (Author) https://creativecommons.org/licenses/by/4.0
Fuzzy Differential Equations with Application in Electrical Circuit
Today, the production and services field faces a change in the competition pattern among independent companies and supply chains. The food supply chain is among the complex supply chains with special characteristics that can toughly be adapted to general evaluation systems. The current research aims to determine the effective indicators for evaluating the performance of the sustainable food supply chain. This research is descriptive-survey in terms of method and practical in terms of purpose. In line with the research implementation, based on the study of the theoretical foundations and the background of the research conducted concerning the effective indicators in evaluating the performance of the sustainable supply chain, the effective criteria were extracted and given to 25 research experts in the form of a questionnaire. Finally, to investigate the relationships between these 26 basic criteria, another questionnaire was prepared and given to the research experts. The final factors were structured based on the answers received and using the methods of fuzzy cognitive mapping and fuzzy DEMATEL. Regarding the centrality criterion in the fuzzy cognitive mapping method, the factors "income distribution, sustainable investment, and average annual training time of employees" have the most centrality, so they were recognized as the main factors influencing the performance evaluation of the sustainable food supply chain.
Copyright (c) -1 Nemat Taghi-Nezhad, F. Amiri, M. Shahini (Author) https://creativecommons.org/licenses/by/4.0
Optimizing green supply chain Networks and U-shaped assembly lines under uncertainty: A meta-heuristic approach
In instances where historical data is unavailable, belief degree-based uncertainty is frequently employed as a substitute for other uncertainty estimation methods, such as interval programming, fuzzy theory, and random planning. The present study focuses on a simultaneous design problem of green supply chain networks and U-shaped assembly lines with two objectives under belief degree-based uncertainty. This is the first study to do so. The objective functions are designed to minimize total transportation and fixed costs of establishing stations, while accounting for air pollution. As this issue is one of the NP-hard problems, a number of meta-heuristic algorithms have been developed for the purpose of addressing multi-objective problems. These include the gray wolf algorithm, the particle swarm optimization algorithm, and the NSGAII algorithm. A novel encoding-decoding method is employed in these algorithms. In order to analyze the performance of the proposed algorithms, test problems from the literature are considered, modified, and completed for this study. The numerical results obtained in this study indicate that the gray wolf multi-objective algorithm exhibits superior efficiency in comparison to alternative algorithms. Finally, the methodology of this study can be utilized as a management tool to address the real-time problems.
Uncertainty theory; Green supply chain; U-shaped assembly line; multi-objective optimization; Meta-heuristic algorithm
Copyright (c) 2025 Nahid Farzan, Ali Mahmoodirad, Sadegh Niroomand, Saber Molla-Alizadeh-Zavardehi (Author) https://creativecommons.org/licenses/by/4.0
A Blended Model Based on Fuzzy Logic for the Calculation of Reynolds Stresses in Turbulent Flows
A novel blended model for turbulent stresses is hereby introduced, combining two well-established turbulence models: the k-ε model and the explicit algebraic stress model of Wallin and Johansson (WJ). The blending of these models can be implemented in two ways: a binary approach, where the k-ε model is used for shear stresses and the explicit algebraic stress model for normal stresses; or a fuzzy approach, which integrates both models in a more flexible manner. The model is implemented by formulating the momentum equations based on the k-ε model and incorporating additional momentum source terms from the blended formulation. This approach leverages the strengths of both k-ε and algebraic stress models, enhancing accuracy while maintaining computational efficiency. CFD simulations of a benchmark case, a backward-facing step, demonstrate that both the binary and fuzzy implementations effectively predict turbulent normal and shear stresses. The findings indicate that the blended model exhibits superior performance in terms of accuracy when compared with both the k-ε model and the explicit algebraic model. Furthermore, the fuzzy approach yielded the most precise results, surpassing those of the k-ε model, WJ model, and binary model. In terms of computational cost, the fuzzy and binary blended models require 1.58 and 1.56 times the CPU time of the k-ε model, respectively, while the WJ model is the most computationally expensive, requiring 1.76 times the CPU time of the k-ε model.
Copyright (c) 2025 Mohsen Fatehi (Author) https://creativecommons.org/licenses/by/4.0
Introducing Two New Classes of Hesitant Fuzzy Soft Sets
A soft set can be defined as a parameterised family of subsets defined over a universal set. While the soft set theory has been demonstrated to be effective in a number of scenarios, in many real-world applications the parameters involved are of greater complexity. This has led to the extension of soft set theory into the framework of fuzzy sets. The employment of fuzzy membership functions has become a common practice in modelling the approximate nature of parameters for fuzzy elements. Beyond ambiguity and uncertainty, practical challenges also arise from experts' hesitancy in precisely defining sets and parameters. This has led to the development of hesitant fuzzy soft sets, which are able to account for such indeterminacy. However, real-world decision-making frequently demands more nuanced modelling. To mitigate errors in representation, it is imperative to address dual perspectives within parameters, concurrently capturing ambiguity and uncertainty, and the conflicting evaluations of decision-makers (e.g., integrating both positive and negative judgments). To address this gap, the paper introduces two advanced frameworks: complex hesitant fuzzy soft sets and bipolar complex hesitant fuzzy soft sets. These models generalise existing concepts by incorporating multidimensional hesitancy and bipolarity into parameter definitions. The paper then goes on to rigorously discuss key definitions, foundational theorems, and practical numerical examples in order to demonstrate the applicability and advantages of these models in decision-making contexts.
Copyright (c) -1 Fatemeh Babakordi (Author) https://creativecommons.org/licenses/by/4.0
Developing an ANFIS model to Assess and Predict Significance of Various Dimensions of Iran's Education Digital Transformation Roadmap
In recent years, the digital revolution in education has had a transformative effect on learning management systems in many countries. The personalisation of education, driven by digital technologies, has led to a significant increase in the importance of creating a strategic roadmap for the effective use of these technologies in both education and agriculture in Iran. It is imperative for policymakers to assess and predict the significance of the dimensions of digital transformation when developing a roadmap. The present research identifies four key dimensions affecting digital transformation: cultural, economic, institutional, and educational. Each dimension, along with its components, was evaluated using ANFIS modelling as input. The study gathered the opinions of 96 participants through targeted sampling, which included educational policymakers, school principals, senior users of digital technology, and managers of knowledge-based companies involved in digital technology for education. The data analysis and calculation results indicated that the cultural dimension ranked first, with an importance score of 4.23. This finding underscores the importance of this dimension and its components in shaping the roadmap for digital transformation in Iran's education system. The model's validity was further confirmed by an error percentage of 0.00015, which was determined through analysis.
Adaptive neural fuzzy inference system (ANFIS)
Copyright (c) -1 Zahra Gholamzadeh, Abbas Khamseh, Mohammadreza Parsanejad (Author) https://creativecommons.org/licenses/by/4.0