Fuzzy Logic Principles Research Articles

Fuzzy Allocation Optimization Algorithm for High-Density Storage Locations with Low Energy Consumptions

The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introduces Fuzzy Allocation Optimization Algorithm to mitigate energy consumption in high storage density settings. It uses the principles of Fuzzy logic to determine the best way to assign the tasks in relation to storage density necessity, urgency and energy consumption. Thus, the proposed approach incorporates fuzzy inference systems with multi-objective optimization methods where location of storage is dynamically assessed and assigned according to energy efficiency parameters. The findings of the simulation and case study prove that the algorithm is successful in saving energy while at the same time lowering storage I/O response time, which provides a viable solution to energy issues in evolving data centres. This work satisfies the lack of energy efficient algorithms in high density storage areas and responds to the recent calls for green technology and smart utilization of resources in the energy field. The findings are used in the promotion of significant IT infrastructures towards developing the next generation of energy efficient data centers with respect to Future Internet and evolving energy web environments.

Traffic Congestion Scheduling for Underground Mine Ramps Based on an Improved Genetic Scheduling Algorithm

The dispatching of trackless transportation on the ramp of underground metal mines is closely related to the transportation efficiency of daily production equipment, personnel, and construction materials in the mine. The current dispatching of trackless transportation on the ramp of underground metal mines is discontinuous and imprecise, with unscientific vehicle arrangement leading to low efficiency and transportation congestion. This paper presents this study, which puts forward a kind of trackless transportation optimization method that can fully make use of the ramp in the roadway, and the slow slope fork point can be used for the trackless transportation vehicle passing section to improve the efficiency of trackless transportation on the ramp. This study adopts the principles of fuzzy logic and uses interval-based positioning instead of real-time positioning to effectively reduce the spatial complexity inherent in the algorithm. At the same time, this research presents a modified genetic algorithm that incorporates a time-loss fitness calculation. This innovation makes it possible to differentiate traffic priorities between different types of vehicles, thus bringing the scheduling scheme more in line with the economic objectives of the mining operations. Various parameters were determined and several sets of simulation experiments were carried out on the response speed and scheduling effect of the scheduling system, resulting in a 10 to 20 percent improvement for different vehicles in the efficiency of underground mining transport operations.

Dimensional Synthesis of the Compliant Mechanism Using the Parametric Fuzzy Form of the Freudenstein Equation

The dimensional synthesis of compliant mechanisms (CMs) leverages the flexibility of their components to achieve precise motion and functionality. This study introduces a novel approach using the parametric fuzzy form of the Freudenstein equation with triangular fuzzy numbers (TFNs) to address the complexities and uncertainties inherent in CM design. By integrating fuzzy logic with advanced computational techniques such as Newton’s method, the proposed methodology offers a robust framework for synthesizing CMs that can adapt to varying conditions. This approach enables the creation of flexible links modeled as fuzzy regions, allowing for optimized performance and reliability across a range of operational scenarios. Numerical examples illustrate the practical application and efficacy of the proposed methods, highlighting significant improvements in the design and synthesis of CMs. The integration of fuzzy logic in the synthesis process not only enhances the resilience of the mechanisms but also paves the way for future advancements in the field. This study demonstrates the potential of fuzzy logic principles in optimizing CM designs, ensuring they meet specific functional requirements with high precision.

Hybrid Neuro-Fuzzy Based Improved Control Design of Electro-Pneumatic Clutch Actuation Control System for Heavy Duty Vehicles

The application of hybrid Neuro-Fuzzy principles of clutch actuation control in enhancing the performance of electro-pneumatic clutch actuation system for heavy duty vehicles is the aim of this presentation. Neuro-Fuzzy is a hybrid design that accommodates the principles of fuzzy Logic and Neural Network in a manner that take advantage of their positive sides. The inability of some heavy-duty vehicles to operate optimally on hilly terrains and which often results in road mishaps are traceable to the inadequacies in conventional clutch actuation control mechanisms. These conventional actuation control techniques provide for routine calibration of clutch actuators after maintenance. Often times, this important requirement of calibration is neglected with attendant ugly consequences. To stem this tide of manual calibration and its obvious defects, an intelligent method of clutch actuation modelled in a hybrid Neuro-Fuzzy control is implemented. Conventional data obtained for errors, speed, torque and power from Mercedes Benz Actros Truck model MP 2, 2031 provided the reference points. These data were embedded into a Simulink block and cascaded into a designed Neuro-Fuzzy Simulink model. Both conventional and Neuro-Fuzzy Simulink model controllers were also simulated. Different percentages of improvements were recorded for piston error, angular speed, engine torque and power respectively. The level and percentage of improvements stood at 0.4821mm or 33.04% decrease for error, increases of 334.1 RPM or 33% for angular speed, 0.0594NM or 33.26% for torque and 2.79W or 16.53% for power respectively.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( ) "GatorTron S 10B" had the 1st rank as compared to ( ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.

METHODOLOGY FOR DEVELOPING INFORMATION TECHNOLOGY FOR SECURITY-ORIENTED MANAGEMENT OF SOCIO-ECONOMIC SYSTEMS BASED ON FUZZY LOGIC

In today's environment, socio-economic systems face increasingly complex challenges associated with high levels of uncertainty, risks, and rapid changes in the external environment. This necessitates the development of effective management methods that can ensure the reliability and flexibility of decision-making. The research is aimed at creating a methodology for the development and application of information technology for security-oriented management based on the principles of fuzzy logic. Fuzzy logic, based on the theory of fuzzy sets developed by Lotfi Zadeh, is a powerful tool for working with incomplete and fuzzy data, allowing to model complex systems and processes where traditional approaches may be ineffective. The paper discusses the peculiarities of using fuzzy logic to create models that take into account possible risks and facilitate informed decision-making even with limited information. The proposed methodology allows to integrate various data sources, process them in real time and provide recommendations for risk management and security of socio-economic systems. The study also describes the structure of the technology, which includes the stages of data collection, fuzzification, fuzzy inference, defuzzification, and decision support. Systems based on fuzzy logic demonstrate high adaptability, resilience to change, and the ability to operate in the face of unpredictable changes. The results of the study confirm that the proposed information technology can be applied in various industries, such as transportation, healthcare, and industry, to improve the safety and efficiency of management decisions. The use of the proposed methodology helps to reduce risks, improve the quality of management and adaptability to changes in external conditions, which are key requirements in modern management.

Predicting the ER Status Using the Gene Expression Profiles

Breast cancer is the most frequently diagnosed malignancy among women globally, making it the leading cause of cancer incidence worldwide. This type of cancer is particularly challenging due to its high degree of molecular heterogeneity, with many different subtypes exhibiting distinct genetic and biological characteristics. Estrogen receptors (ERs) play a crucial role in breast cancer by acting as receptors for the hormone estrogen, often referred to as the hormone receptor (HR). The presence or absence of ERs, known as ER status, significantly impacts treatment decisions, making accurate prediction of ER status essential for personalized treatment plans. Various methods have been proposed in the literature to predict ER status based on gene expression profiles using microarray genome-wide gene expression profiling. The issue of class imbalance, indicated by a notable variation in the number of samples per class and the curse of dimensionality, is a significant concern with microarray datasets. This paper introduces a novel technique, Fuzzy XGBoost, to predict ER status in breast cancer cases while addressing the class imbalance problem. The proposed algorithm enhances the traditional XGBoost method by incorporating fuzzy logic principles, improving its robustness, computational power, and accuracy in handling imbalanced datasets. Our research demonstrates the effectiveness of the proposed Fuzzy XGBoost algorithm in accurately predicting ER status across multiple gene expression profile datasets. This approach not only contributes to advancements in breast cancer treatment but also showcases the power of computational methods in bioinformatics. We evaluate our method using four datasets: GSE2990, GSE3494, GSE6532, and GSE7390, achieving predictive accuracies of 100%, 92%, 100%, and 96%, respectively. The successful application of Fuzzy XGBoost in this context highlights its potential for broader use in bioinformatics, where leveraging computational power to address challenges such as imbalanced data is critical for predictive accuracy and personalized treatment. DOI: https://doi.org/10.52783/pst.619

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

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

Thickness Measurements with EMAT Based on Fuzzy Logic.

Metal thickness measurements are essential in various industrial applications, yet current non-contact ultrasonic methods face limitations in range and accuracy, hindering the widespread adoption of electromagnetic ultrasonics. This study introduces a novel combined thickness measurement method employing fuzzy logic, with the aim of broadening the applicational scope of the EMAT. Leveraging minimal hardware, this method utilizes the short pulse time-of-flight (TOF) technique for initial thickness estimation, followed by secondary measurements guided by fuzzy logic principles. The integration of measurements from the resonance, short pulse echo, and linear frequency modulation echo extends the measurement range while enhancing accuracy. Rigorous experimental validation validates the method's effectiveness, demonstrating a measurement range of 0.3-1000.0 mm with a median error within ±0.5 mm. Outperforming traditional methods like short pulse echoes, this approach holds significant industrial potential.

Comparative study for the performance of pure artificial intelligence software sensor and self-organizing map assisted software sensor in predicting 5-day biochemical oxygen demand for Kauma Sewage Treatment Plant effluent in Malawi

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater.Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents’ temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors.Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi.

Career Interest Assessment: College Students Career Planning Based On Machine Leaning

College students' career planning based on career interest assessment and machine learning integrates advanced technological tools with traditional career counseling methods to provide personalized and data-driven guidance. By utilizing career interest assessment tools, students can explore their strengths, preferences, and aspirations across various career domains. Machine learning algorithms analyze this data to generate tailored recommendations, matching students with career paths that align with their interests and aptitudes. This paper presents a novel approach to career assessment and planning by leveraging the Sugeno Fuzzy Optimized Weighted Machine Learning (SFOwML) framework. The proposed framework integrates fuzzy logic principles with machine learning techniques to provide personalized and accurate career recommendations tailored to individuals' skills, preferences, and values. Through the incorporation of optimization techniques, the framework refines the career assessment model, enhancing its accuracy in predicting suitable career paths for individuals. The practical application of the SFOwML framework offers a valuable tool for career counselors, educators, and individuals seeking guidance in their career choices. The framework incorporates numerical values to quantify individuals' skills, preferences, and values on a scale from 0 to 100, allowing for a more precise evaluation of their career profiles. Through the integration of fuzzy logic principles with machine learning techniques, personalized career recommendations are generated based on these numerical assessments.

Design And Realization of A Comprehensive Quality Assessment System for College Students Based on the Bp Neural Network

Educational evaluation plays a crucial role in ensuring the quality and effectiveness of teaching, learning, and institutional performance. Traditional evaluation methods often struggle to capture the complex and multifaceted nature of educational processes, leading to limitations in assessing quality comprehensively. In response, this paper introduces the Fuzzy Interface Memetic Algorithm for Quality Assessment (FIMA-QA), a novel framework that integrates fuzzy logic modeling with memetic algorithm optimization to provide a nuanced and adaptable approach to educational evaluation. FIMA-QA leverages fuzzy logic principles to represent input variables and assessment criteria in linguistic terms, allowing for the expression of qualitative relationships and uncertainties inherent in educational assessment. Through memetic algorithm optimization, FIMA-QA iteratively refines fuzzy rule bases to enhance assessment accuracy and reliability over successive generations. This combination of fuzzy logic modeling and evolutionary optimization enables FIMA-QA to effectively evaluate various dimensions of educational quality, including teaching effectiveness, student engagement, learning outcomes, and institutional performance. Empirical studies demonstrate the efficacy of FIMA-QA in capturing the qualitative nature of assessment criteria and providing accurate and comprehensive evaluations of educational processes. For instance, teaching effectiveness assessments ranged from 7.5 to 9.8, student engagement from 8.2 to 9.5, learning outcomes from 8.0 to 8.9, and institutional performance from 8.7 to 9.4, showcasing the numerical precision of FIMA-QA.

Integration of Social Network Analysis in the Evaluation of Advertising Communication Effectiveness and Legal Compliance

A fuzzy genetic algorithm (FGA) is a hybrid optimization technique that combines the principles of fuzzy logic with genetic algorithms (GAs). Both fuzzy logic and genetic algorithms are powerful tools for optimization and decision-making in complex, uncertain environments. A Fuzzy Genetic Algorithm (FGA) can be a valuable approach for Social Network Analysis (SNA), where the complexity of social interactions often involves uncertainty and imprecision. This paper proposes an innovative approach to evaluate advertising communication effectiveness and legal compliance by integrating Social Network Analysis (SNA) with Sugeno Fuzzy Genetic Network Analysis (SF-GNA). By combining SNA's insights into communication dynamics and SF-GNA's ability to model complex relationships, the proposed framework offers a comprehensive assessment of advertising campaigns. The integration of SNA enables the identification of influential nodes and communication pathways within social networks, shedding light on the dissemination and reception of advertising messages. Concurrently, SF-GNA facilitates the analysis of legal compliance by assessing the fuzzy relationships between advertising content and regulatory requirements. Social Network Analysis identified key influencers within the network, with nodes A, B, and C demonstrating high centrality scores of 0.85, 0.79, and 0.76, respectively. SF-GNA evaluated the effectiveness of advertising messages, with a membership value of 0.92 indicating strong alignment with consumer preferences and engagement. SF-GNA analyzed the compliance of advertising content with regulatory standards, yielding a compliance score of 0.88, indicating adherence to legal requirements. With Specific regulatory aspects, such as truthfulness and data privacy, were evaluated with membership values of 0.90 and 0.85, respectively, indicating robust compliance.

A Novel Approach to Evaluating College English Teaching Quality Through Dependency Factor Analysis, Incorporating Point Feature Probability Classification Dependency Factor (Pfp-Df)

A Fuzzy Genetic Algorithm (FGA) can be a valuable approach for Social Network Analysis (SNA), where the complexity of social interactions often involves uncertainty and imprecision. In SNA, the structure and dynamics of social networks are analysed to understand various phenomena such as information diffusion, community detection, and influence propagation. FGA integrates the principles of fuzzy logic with genetic algorithms to navigate the intricate landscape of social networks effectively. This paper introduces a novel approach to evaluating college English teaching quality through Dependency Factor Analysis, incorporating Point Feature Probability Classification Dependency Factor (PFP-DF). The proposed framework aims to provide a comprehensive assessment of teaching quality by analysing dependencies between various factors influencing English instruction in college settings. Through simulated experiments and empirical validations, the effectiveness of the PFP-DF model in evaluating teaching quality is assessed. Results demonstrate significant improvements in accuracy and granularity compared to traditional evaluation methods. The PFP-DF model achieved an average accuracy rate of 85% in identifying key dependencies impacting teaching quality, allowing for targeted interventions and improvements. Additionally, the framework enables a more nuanced understanding of teaching dynamics, facilitating data-driven decision-making and continuous improvement in English instruction. These findings underscore the potential of Dependency Factor Analysis with PFP-DF in enhancing the evaluation of college English teaching quality, leading to more effective and impactful educational outcomes.

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.

A novel fuzzy logic-based approach for textual documents indexing

In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.

Application of big data analysis in water pollution monitoring

Contemporarily, the quick development of an abundant amount of big data analysis technologies has brought great convenience to individuals' everyday existence. In terms of environmental protection, especially water pollution monitoring, this technological progress is particularly critical. As the global demand for clean water resources grows and global industrialization intensifies pollution of the water environment, the adoption of advanced data analysis technologies has become critical. Among the vast array of machine learning architectures, three particularly stand out due to their significance and widespread adoption: the artificial neural network (ANN), which serves as a foundational pillar in understanding complex data patterns; the multi-layer perceptron neural network (MLPNN), a sophisticated evolution that allows for deeper computations and learning; and the adaptive neuro-fuzzy inference system (ANFIS), which brilliantly combines neural and fuzzy logic principles for intricate problem solving. These models not only have high accuracy due to their wide application, but they still have their own limitations. This article aims to introduce the methods, basic principles, and application scenarios of these models. In addition, this article also compares the advantages and limitations of these machine learning models, thereby providing some new ideas for future improvements and innovations in model algorithms, application scenarios, and integration.

SOLVING A TRANSPORT PROBLEM WITH FUZZY TARIFFS ON THE EXAMPLE OF CARGO TRANSPORTATION AT THE ROGUN HPP

This article discusses the solution of the transport problem using fuzzy tariffs on the example of cargo transportation of the Rogun HPP. The transport task is an important logistics task, where it is required to optimize the delivery of goods from source to destination with minimal costs. The effectiveness of the proposed methods was evaluated on the practical example of cargo transportation of the Rogun HPP. The results showed that the use of fuzzy tariffs makes it possible to increase the efficiency of the cargo transportation process, especially in conditions of uncertainty or insufficient information. The article proposes and implements methods for solving the transport problem with fuzzy tariffs. They are based on the principles of fuzzy logic, which allows us to take into account uncertainty and various factors affecting the process of cargo transportation. The methods include the formalization of fuzzy tariffs, the determination of weighting factors to assess the importance of each tariff, as well as optimization algorithms to find the best cargo transportation plan. To test the effectiveness of the proposed methods, a specific example was given with cargo transportation data from the Management of the Rogun HPP. The use of fuzzy tariffs allows you to take into account various factors that can affect the process of cargo transportation, such as weather conditions, road conditions and others. This allows for more accurate and flexible planning and management of cargo transportation, as well as taking into account the interests of all parties – the customer, the carrier and the recipient. Some problems and limitations related to the use of fuzzy tariffs in solving the transport problem were also identified. One of the main problems is the difficulty of defining and evaluating fuzzy tariffs. It is also necessary to take into account possible restrictions on the volume of transportation and the availability of vehicles.

Time Series Forecasting during Software Project State Analysis

Repositories of source code and their hosting platforms are important data sources for software project development and management processes. These sources allow for the extraction of historical data points for the product development process evaluation. Extracted data points reflect the previous development experience and allow future planning and active development tracking. The aim of this research is to create a predictive approach to control software development based on a time series extracted from repositories and hosting platforms. This article describes the method of extracting parameters from repositories, the approach to creating time series models and forecasting their behavior. Also, the article represents the proposed approach for software project analyses based on fuzzy logic principles. The novelty of this approach is the ability to perform an expert evaluation of different stages of software product development based on the forecasted values of interested parameters and a fuzzy rule base.