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The objective of this research is to design an optimal model for Build-Operate-Transfer (B.O.T) contracts, considering uncertainty conditions. In this study, mathematical modeling was conducted using MATLAB software and the Particle Swarm Optimization method. Hypothetical data related to a combined cycle power plant project were analyzed as a case study. The construction cost of the power plant was estimated at $120 million, and it was projected that the project's annual revenue would increase from $10.74 million in the fifth year to $46.64 million by the eighteenth year. Operating costs also rose from $1.68 million in the fifth year to $19.90 million by the thirtieth year. The results showed that the cumulative net financial flow for the government reached $273.32 million by the thirty-third year, while the private sector's cumulative net financial flow increased to $157.66 million by the thirty-second year. The proposed model, using historical data and information obtained from similar projects, was able to reduce risks associated with revenue fluctuations and provide a more accurate prediction of annual profits. Based on the analysis, the internal rate of return (IRR) was calculated at 12% for the government and 25% for the private sector. Using the proposed model, the economic lifespan of the project was estimated to be 33 years from the government's perspective and 32 years from the private sector's perspective. The optimal point for transferring ownership of the project was determined to be in year 20.8. The findings indicated that the proposed model for B.O.T contracts, by reducing uncertainty and accurately forecasting financial flows and profits, serves as a suitable tool for improving financial decision-making in infrastructure projects. [ABSTRACT FROM AUTHOR]

A review of existing methods for assigning and regulating operating modes for groups of wells shows that they do not always allow to obtain satisfactory results, since the complexity and unpredictability of the behavior of the reservoir system and the supply system require approaches that would take into account the uncertainty of conditions. In this article, when assigning injection modes, a probabilistic decision-making and a decision-making problem are used under conditions of uncertainty for a group of 15 possible wells of the VII formation of the considered field. The choice of a rational injection strategy under risk conditions is made based on the following considerations: the known probabilities of each strategy result determine the mathematical expectation of each strategy utility. The injection strategy maximizing the expectation of utility is chosen. [ABSTRACT FROM AUTHOR]

Primary care physicians played an important role in the global response during the COVID-19 pandemic, but with the absence of laboratory and diagnostics services, the move to telehealth and the focus on respiratory assessment, they faced increased uncertainty when making clinical decisions. This paper aims to examine the impact of the pandemic on decisions made by primary care physicians, as measured by referrals to chest X-ray and laboratory tests and by prescriptions of antibiotics. We conducted a retrospective study of all visits recorded with fever or cough, presenting to 209 community clinics in Southern Israel during the years 2018–2022. We describe changes in outcome rates across time and use multivariate generalised linear mixed effects model to compare the odds of referrals and prescriptions between periods, while accounting for gender, age, clinic sector, visit type, diagnosis, and season. In total, 609,823 visits to primary care physicians complied with the cohort definitions. Social restrictions were associated with a decline in all measured outcomes for primary care physician decisions, most prominently among ages 20-59, for throat culture referral during the first lockdown (OR = 0.46) and for cephalosporine prescription during the second lockdown (OR = 0.55). This trend persisted following the cancellation of the restrictions. Despite higher uncertainty during the COVID-19 social restrictions, the overall course of clinical decision-making processes was maintained, and was associated with a reduction in the use of auxiliary resources, which can improve the quality of patient care by lowering costs and supporting prevention of future antibiotics resistance. [ABSTRACT FROM AUTHOR]

As society becomes increasingly concerned with sustainable development, the demand for high-efficiency, low-cost, and green technology makes air–land multimodal transportation one of the effective means of fast freight transportation. In the actual transportation business, some orders will have overlapping transportation routes, and transporting each order separately will result in resource waste, high costs, and carbon emissions. This paper proposes a multimodal transportation scheme optimization model considering order consolidation to improve transport efficiency and reduce costs and carbon emissions. An improved genetic algorithm incorporating the ride-sharing scheduling method is designed to solve the model. The results show that order consolidation will reduce multimodal transport costs and carbon emissions but increase transportation time slightly, and the advantages in cost and carbon emission reduction will vary with origin–destination scenarios, which are ranked in order of single-origin single-destination, single-origin multi-destinations, multi-origin single-destination, and multi-origin multi-destination. For the fourth scenario, the cost and carbon emissions decrease by 16.6% and 26.69%, respectively, and the time increases by 5.56% compared with no consolidation. For the sensibility of customer demands, it is found that order consolidation has the advantage for price-sensitive, time- and price-sensitive, and time- and carbon emission-sensitive customers; however, it is specifically beneficial for time-sensitive customers only in single-origin single-destination scenarios. [ABSTRACT FROM AUTHOR]

The decision-making process of wind–photovoltaic–hydropower systems involves knowledge from many fields. Influenced by the knowledge level of the decision-maker and the attribute information of the scheme set, there exists a certain uncertainty in the indicator weights. In view of this, this paper proposes a stochastic multi-criteria decision-making framework for scheduling of wind–photovoltaic–hydropower systems, which overcomes the difficulty of uncertainty in indicator weights or even completely unknown information about indicator weights at the time of decision-making. The Stochastic Multi-criteria Acceptability Analysis (SMAA) theory and the VIKOR model are introduced, and the proposed SMAA–VIKOR model makes the indicator weight space explicit. The study shows that the proposed SMAA–VIKOR model can overcome the obstacle of decision-makers’ lack of information on indicator weights. The ranking acceptability indicators calculated by the model show a more obvious trend of advantages and disadvantages, which gives full confidence to the decision-making group to formulate a plan to be implemented. It breaks through the bottleneck of group decision-making, which is difficult to make effective decisions due to the condition of incomplete information, and enriches the library of stochastic multi-criteria decision-making methods for the scientific formulation of scheduling schemes of wind–photovoltaic–hydropower systems under uncertainty conditions. HIGHLIGHTS A stochastic multi-criteria decision-making framework for wind–photovoltaic–hydropower systems is proposed.; The SMAA–VIKOR model is proposed to clarify the indicator weight space.; The proposed SMAA–VIKOR model can overcome the obstacle of decision-makers’ lack of information on indicator weights.;

The article presents a study on the management of accounting systems, its current state, as well as on the difficulties that arise in the work of enterprises under the risks’ influence. The impact of the Covid-19 pandemic has been studied on the economic activity of the enterprise and on the reporting formation. The main changes in the organization of the company’s activities in modern conditions and costs affecting the accounting statements have been reflected. The accounting problems in conditions of uncertainty and violation of information exchange between responsibility centers have been identified. The existing systems’ comparative analysis of accounting, management accounting and risk management has been compiled. The benefits of optimizing accounting systems under the influence of modern risks have been formulated and information on them has been detailed, in accordance with the specifics of conducting financial and business operations at enterprises. The interrelation of accounting and risk management systems has been established, aimed at minimizing losses in the implementation of the enterprise’s activities in conditions of uncertainty.

Purpose. The tasks of data classification and optimization of order flow management in service systems are widespread. The development of an intelligent information technology (IIT) for optimal management of order flows in service systems (OMFS&S), taking into account imprecisely defined and natural language data characteristics (IDD), implemented on the basis of a modified Hamming network (MHN), is currently relevant, scientific and practical. The main purpose of the work is to develop and improve mathematical models and procedures of the OMFS&S and the formation of IIT based on the MHN with IDD. Methodology. New formulations of the tasks of the OMFS&S, which are characterized by the IDD, are proposed. Mathematical models and intellectual procedures for optimizing the flows of OMFS&S based on MHN have been improved. Software tools for IIT based on MHN and procedures of OMFS&S processes were developed. Numerical studies of the correctness and efficiency of solutions were carried out. Findings. New task formulations of the OMFS&S according to the IDD were formed, which differ in the ability to take into account the results of the choice of controls in the previous steps. Improved mathematical models and productive intellectual procedures of the OMFS&S based on MHN were developed, and the scope of their application was analyzed. IIT software tools for the processes of OMFS&S with IDD were developed and studied, and a numerical experiment was conducted to confirm the reliability and efficiency of the proposed models and methods of OMFS&S processes. Originality. The paper improves mathematical models and productive intelligent procedures for optimizing flows based on the results of classification by MHN. Variants of models for the functioning of OMFS&S procedures have been developed, where flow elements are considered either in isolation from others, or the optimal control choice for the current element affects the maintenance of subsequent elements. Practical value. The intelligent information technology developed on the basis of modified Hamming networks allows optimizing the management of order flows in service systems with imprecisely defined and natural language data characteristics.

Introduction. In modern social and educational conditions, the process of uncertainty in pedagogical practice occurs at a rapid pace, due to the globalisation and digitalisation of education, as well as the COVID-19 pandemic. As a result, there are teachers’ transformations and new functions, which cannot affect their pedagogical capabilities; therefore, the article presents a problem on this issue.Aim. The aim of this problematic article is to consider from different points of view the phenomenon of uncertainty in pedagogical activity and the transformation of the functions of higher school teachers in the conditions of uncertainty caused by the digitalisation of education.Methodology and research methods. The work was based on the methodology of interdisciplinary analysis of theoretical sources, foreign and Russian pedagogical experience in the field of uncertainties; the method of questioning and interviewing teachers of higher education was used to identify attitudes to professional activity in conditions of uncertainty.Results and scientific novelty. In the course of the study, it was found that at the present stage of education development, the transformation of the functions of higher school teachers in conditions of uncertainty is not investigated either in Russian pedagogical practice or abroad due to the undeveloped theoretical provisions in this area of pedagogical activity. The problem is posed in the field of transformation of the capabilities and functions of higher school teachers in conditions of uncertainty from the perspective of different sciences. The scientific novelty of the research is the following developed provisions: 1) a new interpretation of the concept of “professional functions of a teacher” is proposed, including professional competence and professional activity of a teacher in a digital educational environment within his/her subject area at the university; 2) a pedagogical model of transformation of teacher functions in a digital educational environment is developed, including the functions of teaching, upbringing, methodological function, as well as the function of digital didactics and the digital competence function of the teacher; 3) the uncertainty factors and their content that influence the process of transformation of the functions of higher school teachers are identified: technological management of pedagogical activity; pedagogical practice without emotional contact between the subjects of the educational process; self-realisation in a virtual environment; the need for design thinking; personal growth of a teacher in a digital educational environment; transformation of the functions of managing their own pedagogical practice; 4) a classification of uncertainties encountered in the professional activities of higher school teachers has been developed based on two characteristics – belonging to professional educational activities and digital support of pedagogical practice.Practical significance. The proposed classification and uncertainty factors influencing the process of transformation of the functions of higher school teachers can be used in pedagogical practice to determine internal and external influences on teachers in the digital educational environment.

Multimodal transport has emerged and applied to logistics work as single-mode transport was unable to meet the growing demand for logistics. Due to the difference in cost and time consumption between different modes of transportation, the path planning of multimodal transport is different from that of single-mode transport. This paper firstly established a multimodal railroad consignment path optimization model under the condition of transportation time uncertainty and then optimized the path and transportation mode with the particle swarm optimization (PSO) algorithm according to the path optimization model. In addition, the PSO algorithm was optimized by a genetic algorithm to avoid the optimization process of the PSO algorithm from falling into the locally optimal solution. Finally, simulation experiments were carried out on the improved PSO algorithm, and it was compared with the traditional PSO and genetic algorithms. The results showed that the improved PSO algorithm converged to stability after about 200 iterations, the traditional PSO algorithm converged to stability after about 240 iterations, and the genetic algorithm converged to stability after about 400 iterations; the total transport cost and time of railway transportation were USD 114,300 and 52.5 h; the total transport cost and time of the path obtained by the genetic algorithm were USD 60,400 and 35.6 h; the total transport cost and time of the path obtained by the traditional PSO algorithm were USD 37700 and 24.3 h; the total transport cost and time of the path obtained by the improved PSO algorithm were USD 3,1900 and 23.4 h.

This study presents a systemic and causal model of integrated stakeholder risk management of industrial facilities under sustainable development conditions. This model allows us to analyze the main factors of stakeholder influence, namely personnel risks, conflicts, and behavioral economic factors on a scientific project. This method is based on the identification of stakeholders and determining the possibility of the presence in their activities or inaction of personnel risks, conflicts, and behavioral economic factors that can affect the success of the production of industrial facilities, as well as on the calculation of toxicity indicators for each stakeholder. This study presents information technologies for the integrated management of industrial facilities in the context of sustainable development and transition to a circular economy, which, under conditions of uncertainty, allow the manager of an industrial enterprise and his team to implement the methodology of integrated management of industrial facilities in the context of sustainable development and transition to a circular economy to ensure the successful and timely implementation of these projects to meet the needs of stakeholders. [ABSTRACT FROM AUTHOR]

This paper considers the main characteristics of goal-oriented behavior displayed by autonomous intelligent systems in conditions of a problem environment different in the degree of a priori uncertainty. A model is developed for declarative knowledge representation in autonomous intelligent systems regardless of the specific subject area based on active and passive fuzzy semantic networks. The operations of comparing fuzzy semantic networks with each other are considered, which enable the organization of effective decision making in the process of planning goal-oriented behavior under conditions of uncertainty. The operations of decomposition, composition, and generalization of fuzzy semantic networks were developed, which serve to organize the behavioral planning for autonomous intelligent systems in the process of solving complex problems, accompanied by a formal description of current situations of a problem environment with a large dimension. [ABSTRACT FROM AUTHOR]

The aim of the article is to designate catalogues of typological disruption as an inherent element of supply chain management in the light of uncertainty conditions. By means of deduction, the typological characteristics of the disruption and the catalogue of management decisions were determined. In this way, the scope of theoretical knowledge based on the theory of risk and supply chain management was expanded by leading the discourse towards the location of the so-called required uncertainty in the canon of obligatory components of management strategies. The article presents an overview discourse leading to the conclusion that contemporary supply chain management requires at least an attempt to forecast the effects of uncertainty conditions, including the risk of disruption. [ABSTRACT FROM AUTHOR]

Purpose. The article is aimed to improve the automated systems operation of the railway technical system parks and switch D.C. electric motors (EMs), taking into account all uncertainties. Methodology. Solution of the problem was obtained through the development of the model and the method for optimal planning for the EMs set operation. The method is based on the information technology with the possibility to assess the parameters of the current and the predicted state of EMs based on their individual models. The models are built both for individual EMs and for the specified groups. The factors of non-determinism in the model are calculated based on the Hurst index. The task of planning is solved as calculating the optimal sequence of the EM facilities services, which provides a minimum of the total expected operating costs. Findings. The analysis of the main known models, the automated technologies and the systems of EM (ASEM) park operation on the basis of the remote monitoring was done in the research. Based on the practice of the EM park maintenance the new category of the analysis objects was proposed – the service group (SG). The new procedure for the processes classification was developed based on using the Hurst index to improve the reliability of EM and SG individual models forecasting. The technological and the economic model for planning the EM parks operation was created. The article presents the results of the developed automated data management system based on the improved model for the operation planning of the D.C. EM parks. The optimal planning model ensures the minimization of the expected operating costs for the EMs operation, due to the selection of the EM groups service queue. The specialized procedure is used to classify non-deterministic EM remote monitoring data during planning, which allows increasing the accuracy of forecasting the object state parameters. Origilnality. The article describes development of the mathematical model and the information technology for the remote monitoring of the railway technical systems park operation, the railway switch EMs based on the formation of EM and SG individual models, as well as on the evaluation of their current and predicted states, taking into account random factors. The proposed model of the optimal planning as the possibility to choose the SG service queue differs by the group maintenance of the EM facilities, as well as application of the specialized procedure for classifying EM monitoring data. Practical value. The practical value of the results is determined by the provision of the new opportunities for the group optimal planning of the EM service based on the criterion of the minimum expected costs. The procedure for the monitoring data classification of the operational processes makes it possible to increase the reliability of the forecasting antipersistent time sequences results. It also provides an interpretation of the observational data classification results based on the need for practical usage.

We consider the construction of an adaptive algorithm for solving a boundary value problem ensuring that a trajectory issuing from some point hits a finite-size target at a given time under partial uncertainty in the disturbance field. Although the disturbance field has a component that is unknown in explicit form but is still important for hitting a target of given size, we have constructed an iteration procedure for solving the problem in finitely many steps under a number of conditions. The algorithm is based on trial trajectories and uses the measurement of their deviations from the target center as a feedback; this proves sufficient to compensate for the incompleteness of information about the external disturbance field. [ABSTRACT FROM AUTHOR]

There are issues such as limitation of blood resources, perishability, special preservation conditions of blood products and high wastage and shortage costs of blood products, make management of blood products consumption as one of the most complex issues in healthcare systems. This paper proposes a linear mathematical model to optimize consumption of the blood products in hospitals. In the proposed model, in addition to distribution of blood products between hospitals by blood center, possibility of lateral transshipments between hospitals considered as a strategy to deal with demand uncertainty. Furthermore, factor of blood freshness and the need of some surgeries for fresh blood considered in this model as real conditions. Finally, to validate the proposed model, a case related to one blood center and eight hospitals in Tehran city presented and its results discussed

Stay cables exhibit both great slenderness and low damping, which make them sensitive to resonant phenomena induced by the dynamic character of external actions. Furthermore, for these same reasons, their modal properties may vary significantly while in service due to the modification of the operational and environmental conditions. In order to cope with these two limitations, passive damping devices are usually installed at these structural systems. Robust design methods are thus mandatory in order to ensure the adequate behavior of the stay cables without compromising the budget of the passive control systems. To this end, a motion-based design method under uncertainty conditions is proposed and further implemented in this paper. In particular, the proposal focuses on the robust design of different passive damping devices when they are employed to control the response of stay cables under wind-induced vibrations. The proposed method transforms the design problem into a constrained multi-objective optimization problem, where the objective function is defined in terms of the characteristic parameters of the passive damping device, together with an inequality constraint aimed at guaranteeing the serviceability limit state of the structure. The performance of the proposed method was validated via its application to a benchmark structure with vibratory problems: The longest stay cable of the Alamillo bridge (Seville, Spain) was adopted for this purpose. Three different passive damping devices are considered herein, namely: (i) viscous; (ii) elastomeric; and (iii) frictions dampers. The results obtained by the proposed approach are analyzed and further compared with those provided by a conventional method adopted in the Standards. This comparison illustrates how the newly proposed method allows reduction of the cost of the three types of passive damping devices considered in this study without compromising the performance of the structure. [ABSTRACT FROM AUTHOR]

Interval analysis procedures are used to estimate the parameters of an experimental chemical process under conditions of noise and uncertainty in the probabilistic characteristics of the measurement errors for a small measurement sample. Interval analysis makes it possible to describe exactly the set of admissible values of the estimated parameters needed for correct organization of the technological process through a correct choice of its parameters. Approximate estimates of the parameters are obtained by formal application of a statistical approach and it is shown that in this case the standard statistical approach yields essentially meaningless estimates for the parameters of the process studied here. [ABSTRACT FROM AUTHOR]

Stay cables exhibit both great slenderness and low damping, which make them sensitive to resonant phenomena induced by the dynamic character of external actions. Furthermore, for these same reasons, their modal properties may vary significantly while in service due to the modification of the operational and environmental conditions. In order to cope with these two limitations, passive damping devices are usually installed at these structural systems. Robust design methods are thus mandatory in order to ensure the adequate behavior of the stay cables without compromising the budget of the passive control systems. To this end, a motion-based design method under uncertainty conditions is proposed and further implemented in this paper. In particular, the proposal focuses on the robust design of different passive damping devices when they are employed to control the response of stay cables under wind-induced vibrations. The proposed method transforms the design problem into a constrained multi-objective optimization problem, where the objective function is defined in terms of the characteristic parameters of the passive damping device, together with an inequality constraint aimed at guaranteeing the serviceability limit state of the structure. The performance of the proposed method was validated via its application to a benchmark structure with vibratory problems: The longest stay cable of the Alamillo bridge (Seville, Spain) was adopted for this purpose. Three different passive damping devices are considered herein, namely: (i) viscous; (ii) elastomeric; and (iii) frictions dampers. The results obtained by the proposed approach are analyzed and further compared with those provided by a conventional method adopted in the Standards. This comparison illustrates how the newly proposed method allows reduction of the cost of the three types of passive damping devices considered in this study without compromising the performance of the structure.

In the present paper, the types of decisions being taken at the top management level of a company, aim at its development and its manifestation on the competitive market, in the long run. These strategic decisions are adopted more in the conditions of risk and uncertainty determined by the forecast of market evolution. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to explain the measures of risk which a manager can use when making decisions about the most optimal results and long-term objectives of the business enterprise. Efficiency of business operations is ability to achieve results and business goals. Achieving results refers to the concept of short-term effectiveness and ability to achieve the objectives of the long-term aspect of effectiveness. A manager who makes decisions on various business enterprises has a very important role in achieving the efficiency of enterprise. Decision-making can be in terms of certainty and uncertainty. In terms of certainty, a manager knows exactly the outcome of the decision and in conditions of uncertainty over possible outcomes. Therefore, it is necessary to use methods for assessing risk in managerial decision-making, which we explained in our paper. Risk is defined as a condition in which there is more than one possible outcome of a decision in which the probability of each possible outcome is known or can be assessed. These measures help managers to assess risk and, based on that, make the most optimal decision for the company.

In the supply chain, return management is applied in reverse logistics. For various reasons, the flow of materials in the opposite direction of the chain is inevitable. It is essential to deal with the issue of reverse logistics network and efficient management and guidance. According to the reviews, one of the issues that has a great impact on reverse logistics network modeling is the uncertainty situation. In reverse logistics, parameters such as capacity of centers, demand, cost and quality, etc. are uncertain. In this regard, in this study, a possible mixed integer linear programming model for reverse logistics network design is presented. To solve this type of model, it must first become a definitive model. The model presented in this research is multi-product and multi-category that includes transportation costs and facility construction at the same time. The model is NP-Hard in terms of cost minimization (NP-Hard) in terms of cost minimization (facility deployment costs and shipping costs) and uncertainty in demand for return products. Exponentialism increases with respect to the dimensions of the problem. Therefore, in this study, an efficient method using Genetic Algorithm with priority-axis coding is proposed.

A numerical method to solve a general random linear parabolic equation where the diffusion coefficient, source term, boundary and initial conditions include uncertainty, is developed. Diffusion equations arise in many fields of science and engineering, and, in many cases, there are uncertainties due to data that cannot be known, or due to errors in measurements and intrinsic variability. In order to model these uncertainties the corresponding parameters, diffusion coefficient, source term, boundary and initial conditions, are assumed to be random variables with certain probability distributions functions. The proposed method includes finite difference schemes on the space variable and the differential transformation method for the time. In addition, Monte Carlo method is used to deal with the random variables. The accuracy of the hybrid method is investigated numerically using the closed form solution of the deterministic associated. MSC: 35K10, 65C05, 65M99, 35R60

This paper proposes an application of Rough Set Theory (RST) to the real estate field, in order to highlight its operational potentialities for mass appraisal purposes. RST allows one to solve the appraisal of real estate units regardless of the deterministic relationship between characteristics that contribute to the formation of the property market price and the same real estate prices. RST was applied to a real estate sample (office units located in Directional District of Naples) and was also integrated with a functional extension so-called "Valued Tolerance Relation" (VTR) in order to improve its flexibility. A multiple regression analysis (MRA) was developed on the same real estate sample with the aim to compare RST and MRA results. The case study is followed by a brief discussion on basic theoretical connotations of this methodology. [ABSTRACT FROM AUTHOR]

A numerical method to solve a general random linear parabolic equation where the diffusion coefficient, source term, boundary and initial conditions include uncertainty, is developed. Diffusion equations arise in many fields of science and engineering, and, in many cases, there are uncertainties due to data that cannot be known, or due to errors in measurements and intrinsic variability. In order to model these uncertainties the corresponding parameters, diffusion coefficient, source term, boundary and initial conditions, are assumed to be random variables with certain probability distributions functions. The proposed method includes finite difference schemes on the space variable and the differential transformation method for the time. In addition, the Monte Carlo method is used to deal with the random variables. The accuracy of the hybrid method is investigated numerically using the closed form solution of the deterministic associated equation. Based on the numerical results, confidence intervals and expected mean values for the solution are obtained. Furthermore, with the proposed hybrid method numerical-analytical solutions are obtained. [ABSTRACT FROM AUTHOR]

This paper proposes an application of Rough Set Theory (RST) to the real estate field, in order to highlight its operational potentialities for mass appraisal purposes. RST allows one to solve the appraisal of real estate units regardless of the deterministic relationship between characteristics that contribute to the formation of the property market price and the same real estate prices. RST was applied to a real estate sample (office units located in Directional District of Naples) and was also integrated with a functional extension so-called “Valued Tolerance Relation” (VTR) in order to improve its flexibility. A multiple regression analysis (MRA) was developed on the same real estate sample with the aim to compare RST and MRA results. The case study is followed by a brief discussion on basic theoretical connotations of this methodology.

The article deals with the analysis of the researches conducted in the field of the air traffic flow and capacity management in terminal control areas under uncertainty conditions. Traffic flows indicators, uncertainty factors and air traffic flow management in terminal control areas have been reviewed. Principles of improvement of air traffic flow and capacity management in terminal control areas have been analysed and conclusions regarding Ukrainian aeronautical system have been proposed. [ABSTRACT FROM AUTHOR]