The article examines the concept of the digital thread in additive manufacturing as a foundation for improving the economic efficiency of the production cycle through intelligent support at the design stage. A mathematical model and algorithmic procedure for the «Search and Selection of Knowledge Fragments» stage are proposed within the framework of the authors' development of the MAPE-K adaptive control model by incorporating the stages of search, reuse, and evaluation. It is shown that the most significant potential for increasing the productivity of additive manufacturing is concentrated at the design stage of digital product models and technological processes, where the application of ontological modeling and machine learning methods can significantly reduce labor intensity and improve the quality of decisions made. The study formalizes the task of searching and selecting knowledge fragments based on a combination of semantic and embedding representations. A multi-stage candidate selection procedure is proposed, including attribute filtering, embedding search, re-ranking, constraint validation, and selection based on diversity criteria. This approach allows combining the interpretability of ontologies with the scalability and robustness of ANN mechanisms. The results of the work include a model of the digital thread for additive manufacturing, a detailed description, and a system of mathematical support for the stage of searching and selecting fragments. The obtained results form the basis for building a digital thread for additive manufacturing, oriented towards a self-learning knowledge base and continuous model improvement.
This work is devoted to the study of the organizational system of technological processes, including the identification of typical agents, their target functions and strategies for interacting with each other during the functioning of the system. Within the framework of this study, it is proposed to consider the system of technological processes as a complex organizational system of an interdisciplinary nature with its corresponding features, including a multiplicity of target functions, internal uncertainty, distributed management and limited rationality of social elements. The aim of the work is to develop a method for modeling multi-agent interactions in complex technological processes. In the course of the work, the description of typical agents of the technological process system, their tasks and target functions are given. The result of the work is an ontological model of the organizational system of technological processes, as well as a method for modeling multi-agent interactions in complex technological processes based on it. As part of the approbation of the proposed method, a model of the technological process system in the oil and gas industry is developed. At the next stages of the study, it is planned to develop and programmatically implement algorithms for optimizing technological processes based on a multi-agent approach based on hybrid modeling of technological process systems as a set of processes modeled using simulation modeling and an organizational system of intelligent agents managing processes using a multi-agent approach.
This paper develops a mathematical model of an adaptive business model for a digital enterprise operating in a multi-component environment and proves the conditions for its sustainability. The relevance of the research is determined by the fact that modern organizations are simultaneously exposed to physical, institutional, market, and technological factors; however, existing approaches consider these factors in isolation and do not provide formal justification for viability conditions. The methodological framework of the study integrates active systems theory, system dynamics, and nonlinear control theory. A causal loop diagram and a stock-flow diagram were constructed using the VENSIM simulation environment. The Input-to-State Stability (ISS) concept and the Lyapunov function method were applied for formal stability analysis. As a result, a model with two feedback loops was developed — a stability loop and an adaptation loop, with switching between them occurring through a critical stress threshold. The system was proven to possess ISS stability under three simultaneous conditions: boundedness of the external perturbation flow function, positive sensitivity of adaptation capacity to buffer magnitude, and positivity of the buffer replenishment function during stability periods. Numerical simulation of three scenarios confirmed the theoretical predictions and demonstrated the existence of an equilibrium attraction domain. The results expand the possibilities for quantitative analysis of organizational sustainability and can be applied for early threat detection, investment decision support, and digital platform strategy development.
The paper examines the efficiency indicators of cargo storage in 3PL logistics companies. A system of indicators of the efficiency of cargo placement in a warehouse is presented, divided into groups: an indicator of the efficiency of filling warehouse cells, an indicator of warehouse occupancy and an indicator of the relevance of the warehouse configuration. The indicator of the relevance of the warehouse configuration is of particular value for 3PL logistics companies, as it allows them to assess the compliance of current cell sizes taking into account the changing cargo composition and make strategic decisions about the need to change the warehouse configuration. A logical scheme of making decisions on measures to optimize the placement of goods in a warehouse, depending on the magnitude of the indicators, is presented. The results of the work can be used to build a comprehensive management system for the efficiency of cargo storage in 3PL logistics companies.
This article presents a theoretical and methodological approach to investigating the processes of innovation integration into the educational activities of educational organizations, leveraging simulation modeling tools. Within the framework of a systems approach, an educational organization is conceptualized as a complex dynamic system. This allows for a more detailed analysis of the nonlinear processes involved in innovation implementation and their systemic consequences. Amidst rapid changes in the educational environment and the continuous emergence of new technologies, the importance of effective innovation integration becomes particularly crucial. The central outcome of this work is the development of a theoretical model that elucidates the systemic interconnections among elements of the educational environment under conditions of innovative change. This model facilitates the investigation of how various factors influence the success of innovation implementation and how they mutually interact. The study conducted a comparative analysis of the capabilities of discrete-event simulation, agent-based modeling, and system dynamics. This analysis aimed to assess their applicability for addressing innovation management tasks at various levels, ranging from operational to strategic. Methodological limitations and the complementary potential of each approach were identified. The practical significance of this research lies in establishing a framework for selecting appropriate modeling tools to design and evaluate the implications of innovation implementation within educational organizations.
This paper is devoted to formalizing the task of scheduling in distributed production systems. The paper examines the features of complex production systems, including the uncertainty characteristic of real production systems, the multi-agent nature of the interaction of elements and the multiplicity of target functions, as well as their impact on the parameters of the production schedule. The paper provides an analysis of scientific literature in the field of distributed production system modeling based on scientific publications presented in Scopus and RSCI. Based on the results of the analysis of international experience in modeling distributed production systems, the structural elements of distributed production systems are described in the context of the task of scheduling. The result of the work is an ontological model of the scheduling process in distributed production systems as a set of strategic interactions in the terminology of game theory and multi-agent systems. To describe game interactions, a set of formalized strategies and agent payoffs in a top-down pattern of game interaction is provided, and a description in terms of BDI models of agents of a multi-agent system involved in the production schedule is also provided. An ontological model of the communication network of the structural elements of distributed production systems is also given in the context of the task of scheduling. The results obtained are the basis for the development of a decision support system for scheduling in distributed production systems.
This article analyzes the incentives for industrial growth from the perspective of modern Russia's strategic priorities to create an environment conducive for scientific, technological, and engineering advances. The key national challenges lie in overcoming the technological gap in industry at the regional level, which requires systematic, high-quality upgrading of production facilities. Since the technological capacity of the industrial sector is the fundamental factor in achieving technological sovereignty, the role of leasing as one of the instruments of industrial policy in this process is substantiated. The goal of the work is to improve the scientific and methodological approach to the use of leasing in stimulating industrial development in the context of technological sovereignty. The study focuses on several complex issues, analyzing conceptual approaches to technological sovereignty and industrial policy. Furthermore, we constructed a model for optimizing leasing payments adapted to the conditions of post-conflict industrial recovery in a new region of the Russian Federation based on fostering industrial partnerships. The main result of the study is the calculation of leasing payments taking into account the risk premium and interest rate subsidies, allowing to justify measures for economic stimulation of leasing operations in the industry of a region with specific economic conditions, increasing their mutual benefit for the lessor and the lessee.
The article is devoted to the development of a hybrid approach to modeling inventory management processes. Typical features of inventory management in conditions of variable demand, seasonality and uncertainty of supplies typical for the construction industry are considered. As part of the study, the current state of warehouse and logistics processes was described and typical BPMN diagrams were constructed that reflect the full cycle of material movement: from demand generation and procurement to acceptance, storage and transfer of materials to construction sites. Based on this process approach, it is proposed to create a hybrid simulation model combining discrete event modeling and elements of system dynamics, which will allow taking into account seasonal fluctuations in demand, changes in supply conditions and other external factors. The results of the study are the basis for developing a model of the target state of processes aimed at reducing the risks of shortage and excessive accumulation of materials, as well as improving the efficiency of warehouse and logistics processes. The proposed approach is focused on the formation of a sustainable inventory management system that ensures the reduction of excessive accumulation of materials, reduction of losses and rational use of resources. The use of a hybrid simulation model will allow taking into account both long-term trends in demand and operating conditions of the system, as well as operational features of warehouse processes, which will increase the stability and adaptability of inventory management.
This article is devoted to the analysis of market risks for the development of an effective risk management system in an airline to increase its resilience to crisis situations, as well as minimize the impact of risks on its operations. The study is based on the hypothesis that the effectiveness of implementing a risk management system in the airline directly depends on the quality of the analysis of key production processes. The purpose of the study is to analyze market risks as part of the development of an effective risk management system in the air company to increase its resilience to crisis situations, as well as minimize the impact of risks on its activities. The methodological basis of the study was qualitative and quantitative methods, which assume: 1) construction of the airline's basic production process; 2) formation of the airline's risk register; 3) construction of the airline's risk heat map; 4) formation of an ontological scheme of the stakeholders register for risk management. As a result of the research, key aspects for the development of risk management in airlines have been identified. The integration of risk management systems with quality and safety management systems, digitalization will help to improve the quality of risk analysis, improve decision-making processes in airlines, and adapt to new market requirements.
In the context of digital transformation, the formation of well-balanced project teams has become one of the critical factors for the successful implementation of IT projects. The aim of this study is to identify and compare tools that take into account the specifics of both waterfall and agile project management methodologies. The object of the study is the process of selecting and allocating specialists within IT project teams. The research methodology included a critical review of scientific publications, comparative analysis of team formation models and tools, and their systematization according to their applicability to Agile and Waterfall approaches. The analysis identified five key models (fuzzy cognitive, regression, system analysis, grading and experience-based algorithm, skill trees) and six main tools (expert evaluation, game theory, system analysis, single- and multi-criteria optimization, simulation modeling). The comparison showed that most approaches focus on single criteria optimization, while the use of multi-criteria methods for Waterfall projects remains limited. The results revealed a methodological gap between agile and waterfall practices, particularly in terms of communication and dynamic factors. The study concludes that the development of multi-criteria optimization tools for Waterfall projects is necessary to enhance team stability and predictability. The findings may be useful for project managers, HR specialists, and scholars in the field of project team management.
The digitalization of healthcare has led to a rapid increase in data volume alongside growing demands for privacy and regulatory compliance. In this context, many healthcare organizations face difficulties integrating fragmented information systems while maintaining full control over sensitive data. This paper proposes a conceptual framework for a modular analytics platform designed to support predictive decision-making in medical institutions without direct access to identifiable patient information. The study focuses on the digital transfor mation of healthcare management processes using de-identified institutional data. The methodology combines systems analysis with architectural modeling, resulting in a set of structured diagrams that describe the platform’s deployment logic, component interaction, and business model. The proposed architecture supports both cloud-based and on-premises deployment options, allowing institutions to choose between flexibility and full data sovereignty. The platform includes modules for integration and visualization, along with secure API-based data exchange mechanisms. architectural and BPMN diagrams are presented to illustrate the platform structure and subscription-based financial model. The results demonstrate the feasibility of implementing the proposed architecture in healthcare environments constrained by legal, technical, and organizational factors. The concept provides a foundation for future prototyping and pilot deployment in healthcare systems aiming to achieve secure, scalable analytics.
The article examined the effect of the green premium (greenium) on the corporate bond market of the European Union countries. The relevance of the work is due to the need for empirical evidence of the existence of this award in the context of heterogeneous methodological approaches to its assessment and the increasing role of sustainable financing in the context of the transition to a low-carbon economy. The aim of the work is to identify and quantify the effect for corporate bonds issued by Germany, Spain and the Netherlands, taking into account national macroeconomic specifics and industry specifics of issuers. The research methodology includes the formation of a sample of pairs of comparable instruments, a comparative analysis of profitability and liquidity, an assessment of correlation dependencies and the construction of regression models taking into account macroeconomic indicators and market indicators. The results show that the most significant and statistically significant decrease in yields was found for Spanish issuers, while the effect was statistically less significant for German and Dutch bonds. Regression analysis confirmed the significant impact on profitability of traditional macroeconomic factors, including the inflation rate and the risk-free rate. There was also a statistically significant negative impact of inflation and a positive impact of the risk-free rate on the yield of Spanish green bonds, and the sensitivity to inflation turned out to be significantly higher. The quality of the constructed models was high, as evidenced by the coefficients of determination (R2) at the level of 0.82 for classic Spanish bonds and 0.86 for green bonds of Germany and the Netherlands. The results obtained emphasize the importance of taking into account national specificities when developing investment strategies and shaping public policy for the transition to sustainable development. The practical significance lies in the possibility of using the research results for a deeper analysis and development of a methodology for assessing sustainable financial instruments.
The article addresses the issue of spatial differentiation in the socio-economic development of Thailand’s provinces in the context of the national «Thailand 4.0» strategy. The research problem arises from the limited empirical evaluation of regional heterogeneity that integrates demographic, sectoral, and institutional dimensions. The objective is to identify structural patterns of provincial development and to propose a typology that may serve as a basis for differentiated regional policy. The study relies on provincial-level indicators for 2010 2021, including per capita gross regional product, labor migration, industrial investment, land use, and inbound tourism. The Williamson coefficient was applied to quantify inequality, revealing its growth over the past decade. Clustering was performed using k-means, hierarchical agglomerative methods, and DBSCAN in Python with scikit-learn. The k-means algorithm with three and four clusters produced the most robust results, isolating Bangkok as a distinct cluster. Three persistent groupings were identified: industrial centers in the central region, agricultural provinces of the northern and northeastern areas, and tourism-driven provinces in the south. The analysis also revealed β-convergence processes in several transitional provinces, suggesting gradual alignment of development trajectories. Policy recommendations emphasize modernization of agriculture, innovation support for industrial centers, and infrastructure projects in tourism-intensive provinces. The findings confirm the persistence of spatial polarization and highlight the utility of cluster analysis as a tool for refining Thailand’s regional development strategy.
As part of the current task of predicting employee burnout, machine learning models are being developed to predict burnout based on data regarding the fulfillment of employee expectations from the corporate well-being program. The source data consists of survey results from employees of large companies. To predict the degree of burnout, a classification model based on fuzzy levels of burnout is built. The correspondence matrix between the fuzzy ranges of the integral indicator of expectation fulfillment and the fuzzy levels of burnout is optimized using the criterion of entropy minimization. The task of binary classification for predicting the presence of burnout is also addressed. For this purpose, a set of rules is formed that provides an explanation for the machine learning model. Each rule uses a couple of features. The machine learning model includes 10 decision rules and achieves an accuracy of 80% for burnout prediction. Based on the constructed model, it is concluded that the nature of burnout differs depending on the implementation of corporate well-being activities for different clusters of employees based on expectation level. At the same time, the assignment of an employee to a particular cluster is related to his value priorities. Thus, the study allows for the identification of hidden factors that are determined by the values of employees and affect their burnout.
The study examines the relationship between key macroeconomic indicators and the dynamics of foreign trade relations between Russia and Belarus with BRICS countries from 2004 to 2023. For this purpose, econometric methods and the gravity model of foreign trade were used. The logarithmic values of exports and imports of Russia and Belarus to partner countries were used as dependent variables, while independent variables included the GDP of exporting and importing countries, GDP per capita, bilateral exchange rates and the ratio of trade volume to GDP of the countries in question. The results of the analysis indicated that the export volume of Russia and Belarus with BRICS partner countries was positively related to the GDP volumes of these countries and the GDP per capita volumes of exporting countries. The import volume of Russia and Belarus from other countries was also positively related to the GDP of those countries, the GDP per capita and the exchange rate of importing countries. The study revealed that the ratio of foreign trade turnover to the GDP of partner countries was inversely proportional to the import volume of Russia and Belarus from those countries. This confirmed the hypothesis that the dynamics of exports and imports of Belarus and Russia were largely influenced by the economic development of their partners and the scale of their own economies. In this regard, it is recommended that the governments of Russia and Belarus continue to develop trade relations with BRICS countries to enhance competitiveness in the global market and achieve higher economic development indicators. Further research could consider factors such as distance between countries, technological development and others.
Contemporary conditions of project activity management are characterized by a high degree of dynamism, uncertainty and competition for limited resources. This requires adaptive and intelligent approaches to decision support, especially in the context of project portfolio management. This article proposes an agent-based decision support model based on a multi-agent system (MAC), in which each object of the project environment (portfolio, project, task, resource) is represented as a software agent with autonomous behavior logic. The developed architecture and operational algorithms of the system ensure decentralized decision-making, coordination of agents' actions and adaptation to changing conditions of project implementation. Special attention is paid to the formalization of agent types, scenarios of their interaction, events that initiate decision-making and appropriate algorithms for resource redistribution and task rescheduling. Model verification was conducted through comparison with traditional centralized approaches. The results confirm the effectiveness of the agent-based approach to increase adaptability, consistency and strategic validity of decisions in a multi-project environment.
This article is devoted to the development of a standard model of the life cycle of a knowledge-intensive project at oil and gas enterprises in order to obtain accurate forecasts of project deadlines. The relevance of the work lies in improving the accuracy of the forecast models used by oil and gas enterprises in project management and the corresponding increase in competitive advantages by reducing the risks of project deadline failures. The study analyzed the main stages of the life cycle of knowledge-intensive projects and the specifics of each stage. To develop a standard model, a system-dynamic approach was chosen to visualize cause-and-effect relationships between the variables of the system state and to describe these relationships in the form of structured functional dependencies. The results of the study present a standard system-dynamic model of the life cycle of a knowledge-intensive project in the oil and gas industry, containing the main stages of project implementation from the process of requirements analysis to the implementation and support of project results. The stages of passing the audit review are reflected in the model as auxiliary processes. The model can be used to estimate the project execution time depending on the established values of the influencing parameters at the enterprise under study, for the purpose of making management decisions in project management. The prospects of the study are the introduction of an accounting of the occurrence of investment risks into the model, as well as the possibility of a quantitative assessment of their occurrence.
The article examines the relationship between socio-economic factors and the dynamics of economic development in Thailand’s provinces within the context of sustainable development and pronounced territorial heterogeneity. The aim of the study is to assess the factors of provincial economic development using econometric modeling methods. The analysis covers 77 provinces of Thailand, studied on the basis of panel data for the period 2013−2022. The methodology involves the application of fixed and random effects models, logarithm of variables and Hausman, Breusch-Pagan and Pesaran tests to evaluate model quality. The modeling results revealed statistically significant relationships: population size and the level of industrial investment per employee are negatively related to net provincial product per capita. The negative contribution of these factors indicated the need to reconsider their role and apply spatial analysis to account for regional differentiation and interprovincial interactions. The importance of time-fixed effects allows for consideration of the impact of macroeconomic and political events, including the 2014 coup d'état, the COVID-19 pandemic and structural reforms. The developed model confirms the necessity of a targeted approach to the formation of state policy and regional planning strategies. The findings can be used to develop mechanisms for stimulating sustainable development and improving the efficiency of territorial governance. The study fills a gap in empirical research and can serve as a foundation for further scientific and applied developments.
The current geopolitical picture of the world has become a serious challenge for many countries. In addition to many different turbulent phenomena, changes in logistics and transport routes between countries can also be observed in the global economy of the current period. Along with the changing roles and the struggle for various geopolitical interests, issues of transport communications and transit opportunities for countries are coming to the fore. The problem of developing transport and logistics routes has become even more urgent for Armenia and requires an immediate solution. The key objective of the study was to assess the possibilities of Armenia's transport logistics in terms of its interaction with both countries and third countries, including from the perspective of expanding re-exports over the past few years. The research methodology is based on the analysis of the impact of transport communications on the economic development and well-being of Armenia. Methods of qualitative and statistical analysis, as well as observation and synthesis are used. The information base includes statistics on cargo transportation and the commodity structure of Armenia's trade with the EAEU and third countries. As a result, it was revealed that the existing transport and logistics system of Armenia is poorly developed and significantly hinders the development of the country's economic potential. The main conclusion of the study was the thesis about the need to develop logistics transport systems in order to have a positive impact on Armenia's economic growth and development in the long term.
Allocation of operations to work centers is a key management task in modern production systems. Its relevance stems from the need to optimize the use of limited resources to achieve high efficiency and productivity. However, the problem is complicated by the combinatorial complexity associated with the discrete nature of operations and the need to consider multiple factors, including resource constraints, time constraints, and process requirements. This requires specialized approaches to find efficient solutions. This paper is devoted to analyzing such approaches. The paper considers actual problems of optimization of operations distribution to work centers in modern production systems. Particular attention is paid to the problems of minimizing variable costs associated with the changeover of equipment and increasing the overall efficiency of production processes. The paper considers the limitations of classical optimization methods such as linear programming, a review of modern approaches, including combinatorial algorithms, methods of the theory of schedules. Special attention is paid to heuristic algorithms, such as genetic algorithms, simulated annealing, and ant algorithms, which allow us to find acceptable solutions in a short time. The paper also discusses the key factors affecting the scheduling, such as resource constraints, time constraints, and technological requirements. Considering the conditions of the production problem posed in the paper problem and based on analyzing the advantages and disadvantages of existing methods of its solution, as an alternative it is proposed and substantiated the use of multi-agent approach with the application of heuristic algorithms for the distribution of work operations at production enterprises.
