This paper addresses the problem of estimating passenger demand for flights, with a particular focus on the necessity of developing precise forecasts that incorporate intricate and interdependent variables for effective resource planning within the air transport industry. The present paper focuses on the development of a model for medium-term flight demand estimation by flight destinations. This is based on the analysis of historical airline data on dates, departure times, and passenger demand, as well as the consideration of the influence of macroeconomic indicators, namely gross regional product (GRP), median per capita income, and population of departure and arrival points. This paper reviews international experience in the development of demand forecasting models and their use for resource planning in the industry. The developed model was evaluated using historical data on demand for a single turnaround flight operated by an airline. The developed model allows for the forecasting of the distribution of potential demand for airline flight destinations in the medium term, utilizing comprehensive historical data on departure times and flight demand by destination.

The new coronavirus pandemic has had a major impact on worldwide economic development Many infection-countering measures have imposed restrictions on economic activities. At the same time, economic sectors contribute unequally to both the disease's spread and to regional economic development This article proposes a method for assessing the consequences of partial regional isolation. Simulation modelling and system dynamics are applied to assess both epidemiological and economic consequences. The classic "Susceptible - Exposed - Infected - Recovered" disease spread model has been modified with the addition of a new group: Isolated Individuals. This modified model allows the size of the receptive population and the frequency of their contact to be regulated based on scenarios of partial regional isolation in the context of economic sectors. The model is tested in St. Petersburg. Changes in the region's infected population are forecasted as the result of the establishment and removal of partial isolation measures in the context of individual economic sectors.

This study focuses on the development of a decision support system for complex production systems. As a promising approach to resource allocation challenges, the application of AI tools, particularly the multi-agent approach, is proposed. It is hypothesized that a decision support system based on multi-agent systems (MASs), grounded in an invariant ontology and utilizing game-theoretic tools, will enhance the effectiveness of managerial decisions by accounting for the inherent multi-agent nature of complex production systems, manifested in diverse objective functions and intricate interaction structures. The work identifies key features of intelligent agent interactions in multi-agent systems. Drawing on interaction types relevant to complex production systems and the selected MAS implementation architecture, an ontological model of multi-agent interactions and an ontological model for managing complex production systems are designed. The proposed models are tested using data from an oil and gas enterprise, and a formalization of utility functions for its agents is provided using game-theoretic tools. The resulting ontological model of multi-agent interactions in complex production systems is practical and easy to implement. It enables the design of MASs for diverse complex production systems, as demonstrated through a resource management case study in the oil and gas industry. Future study will refine the ontological model via industry-specific validation and expand the mathematical and computational tools for model implementation.

The article discusses the approach to the conduct and justification of the selection of the project management system and the utilization of labor resources using the method of comparative quantitative assessment of the quality of systems based on the functional completeness criterion to ensure the definition of a suitable system for supporting the activities of the IT company. The article determines the tools for solving the problem of effective project management and ensuring control over the workload of employees, which makes it possible to assess the possibility of personnel working in new projects.

Objectives. The authors conduct an analytical review of available optimization methods and simulation tools to identify their key features, effectiveness, and possible applications. The aim was to form an integrated picture of modern approaches, which may facilitate decision making when selecting the most appropriate method for a particular task. The key objective was to review and classify various optimization tools, which of theoretical and practical value for developers of new models.Methods. Scientific publications and analytical materials were retrieved from specialized databases and technical documentation libraries.Results. The analysis and classification of existing optimization methods allowed the authors to identify their advantages, disadvantages, and application features, as well as to determine the relationship between theoretical concepts and their practical implementation. During the analysis, various optimization approaches were considered, covering both classical and modern simulation methods.Conclusions. The importance of informed selection of optimization methods, which raise the efficiency and accuracy of simulation procedures, is highlighted. The results obtained indicate the need for further study and comparative analysis of the methods used in practice in order to establish their efficiency and applicability in various scenarios. Future research directions include experimental testing of the effectiveness of various approaches based on several models in order to determine their advantages and disadvantages for a more informed selection of the method suitable for a particular task.

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive.