Search For Optimal Solution Research Articles

Applying Ant Colony Optimization to Reduce Tram Journey Times

Nature-inspired algorithms allow us to solve many problems related to the search for optimal solutions. One such issue is the problem of searching for optimal routes. In this paper, ant colony optimization is used to search for optimal tram routes. Ant colony optimization is a method inspired by the behavior of ants in nature, which as a group are able to successfully find optimal routes from the nest to food. The aim of this paper is to present a practical application of the algorithm as a tool for public transport network planning. In urban public transport, travel time is crucial. It is a major factor in passengers’ choice of transport mode. Therefore, in this paper, the objective function determining the operation of the algorithm is driving time. Scheduled time, real time and theoretical time are analyzed and compared. The routes are then compared with each other in order to select the optimal solution. A case study involving one of the largest tramway networks in Poland demonstrates the effectiveness of the nature-inspired algorithm. The obtained results allow route optimization by selecting the route with the shortest travel time. Thus, the development of the entire network is also possible. In addition, due to its versatility, the method can be applied to various modes of transport.

Solving multi-objective robust optimization problems via Stakelberg-based game model

Real-world multi-objective engineering problems frequently involve uncertainties stemming from environmental factors, production inaccuracies, and other sources. A critical aspect of addressing these problems, termed Multi-Objective Robust Optimization (MORO) problems, is the development of solutions that are both optimal and resilient to uncertainties. This paper proposes addressing these uncertainties through the application of Stackelberg game models, a novel approach involving the interaction of two players. The Leader searches for optimal and robust solutions and the Follower generates uncertainties based on the Leader’s chosen solutions. The Follower seeks to tackle the most challenging uncertainties associated with the Leader’s candidate solutions. Additionally, this paper introduces a novel metric to assess the robustness of a given set of solutions concerning specified uncertainties.Based on the proposed approach, a co-evolutionary algorithm is developed. A numerical study is then conducted to evaluate the algorithm by comparing its performance with those obtained by four benchmark algorithms on nine benchmark MORO problems. The numerical study also aims to assess its sensitivity to run parameter variations. The experimental results demonstrate the proposed approach’s effectiveness in identifying a non-dominated robust set of solutions.

Gamification Technology as a Tool for Improving Students’ Understanding of Text-Based Problem-Solving Techniques

Solving text problems to a greater extent than other mathematical topics in Grades 5–6 contributes to the development of erudition, thinking abilities, analytical reasoning skills, and the formation of mathematical knowledge. Problem-solving skills bring students closer to real-life situations that require them to develop strategies and search for optimal solutions. The article considers the necessity of using gamification technology to effectively achieve the main goal of the research – to improve students’ understanding of text problem solving methods. In order to achieve the goal, the authors have developed methodological recommendations to improve students’ understanding of the condition and solution of text problems. The authors note that the use of interactive tools, innovative teaching methods, such as online simulators and gamification, contributes to the attractiveness of the learning process. This helps to increase students’ activity, formation of motivation for understanding the conditions of the textual task, as well as stimulates their cognitive activity. The methods of analysis, synthesis, generalization, study of pedagogical experience, observation and survey have been used to conduct the research. The analysis of the research results showed that the novelty of using gamification technology will help, on the one hand, the teacher to adapt teaching methods depending on the level of understanding and the pace of learning of students and to present them with new material in an interactive form, and on the other hand - students to develop self-education, improve communication skills, learn to discuss the solution and work in a team. The author’s development of methodological recommendations in combination with game technologies will help teachers to apply gamification in the practice of solving text problems with schoolchildren. Keywords: text problem, formation of mathematical knowledge, gamification technology, motivation, game technology

A Novel Swarm Exploring Varying Parameter Recurrent Neural Network for Solving Non-Convex Nonlinear Programming.

Aiming at solving non-convex nonlinear programming efficiently and accurately, a swarm exploring varying parameter recurrent neural network (SE-VPRNN) method is proposed in this article. First, the local optimal solutions are searched accurately by the proposed varying parameter recurrent neural network. After each network converges to the local optimal solutions, information is exchanged through a particle swarm optimization (PSO) framework to update the velocities and positions. The neural network searches for the local optimal solutions again from the updated position until all the neural networks are searched to the same local optimal solution. For improving the global searching ability, wavelet mutation is applied to increase the diversity of particles. Computer simulations show that the proposed method can solve the non-convex nonlinear programming effectively. Compared with three existing algorithms, the proposed method has advantages in accuracy and convergence time.

Optimized k-nearest neighbours classifier based prediction of epileptic seizures

Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this work, k-nearest neighbours (KNN) classifier algorithm is optimised using genetic algorithm for effective classification and faster prediction to meet this requirement. Genetic algorithms search for optimal solutions in complex and large environments. Results are compared with other machine learning models such as support vector machine (SVM), KNN, decision tree classifier, and random forest. With optimization using genetic algorithm KNN was able to achieve an enhancement in accuracy at lower training and testing times. It was observed that the accuracy offered by optimized KNN was 92%. Random forest classifiers showed minimum complexity and KNN algorithm provided faster performance with better accuracy.

An adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection

The feature subset obtained by traditional feature selection algorithms usually contains many irrelevant features and redundant features, which increases the size of the feature set and reduces classification accuracy. More importantly, these irrelevant features and redundant features may lead to local optimum. How to efficiently select the global optimal feature subset from high-dimensional data is a challenging task. To solve the problem, an adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection (ADSCO) is proposed in this paper. The contributions of this algorithm are as follows. Firstly, an adaptive dual strategy constrained optimization mechanism (ADSC) is developed. In the ADSC, the adaptive linear constraint strategy and the random constraint strategy are designed to search for the feature subsets that satisfy the constraints. This can reduce the search space of candidate solutions. Secondly, the dual population-constrained coevolutionary optimizer (CO) is developed to help the algorithm jump out of the local optimum and search for better optimal solutions to further improve the classification accuracy of ADSCO. Finally, the ablation experiments are executed to validate the effectiveness of the proposed ADSC and CO. The ADSCO algorithm is tested on 8 high-dimensional datasets and 8 low-dimensional datasets, and compared with seven typical population feature selection algorithms. Experimental results show that the proposed ADSCO algorithm is superior to the 7 comparison algorithms in terms of classification accuracy and feature subset size, demonstrating its strong competitiveness in tackling high-dimensional data feature selection.

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

With the development of science and technology, high-dimensional global optimization problems have become increasingly prevalent for scientific research and engineering, such as gene recognition, vehicle routing, job scheduling, and network topology. These problems are typically characterized by enormous and complex search spaces and numerous local minima, making it challenging to find the global optimal solution with limited computing resources. This paper introduces an enhanced sparrow search swarm optimizer (ESSSO) based on a bio-mimetic method. The ESSSO employs an adaptive sinusoidal walk strategy based on the von Mises distribution, a learning strategy utilizing roulette wheel selection, a two-stage evolution strategy, and a selection mutation strategy to address these issues. The proposed sinusoidal walk strategy, grounded in the von Mises distribution, supports a balanced evolutionary search. This mechanism disperses the individuals in a swarm in various directions based on a circular normal distribution. It then leads the search and adaptively adjusts their step sizes according to the size of the search domain during each generation of evolution. The learning strategy, based on roulette wheel selection, enhances the diversity of the population and improves the global search capability of the algorithm during the initial iterations. The two-stage evolution strategy involves a sine-learning mechanism based on the von Mises distribution and an adaptive mutation mechanism. The former is designed to boost the convergence speed of ESSSO, while the latter prevents ESSSO from getting trapped in a local optimum. Additionally, the selection mutation strategy further enhances convergence speed while maintaining population diversity. These strategies promote exploration in the early stages of evolution and exploitation in the later stages, enabling a well-balanced search for optimal solutions. We conducted comprehensive experiments two standard benchmark sets (i.e., CEC2010 and CEC2013), antenna array optimization, feature selection, and four engineering design problems. The results indicate that ESSSO outperforms ten comparison algorithms, especially in scenarios with smaller population sizes. This confirms its effectiveness in high-dimensional global optimization tasks and demonstrates that it can achieve better results with less computational resource consumption.

Searching for optimal solutions in a landscape fragmentation assessment: A case study from Poland – Identification of spatial data and methods

There is no single correct or optimal scale for describing spatial landscape heterogeneity. However, since landscape patterns are scale-dependent, using different scales in the analysis may result in different outcomes, which can affect landscape management options. The main goal of our research was to identify the most adequate GIS solution for converting vector data to raster data while keeping the spatial continuity of most linear features and input land cover data as detailed as possible. In addition, to compare our database with the CORINE Land Cover data approach, we used 3 different rasterization methods and raster data with 100- and 50-m pixel sizes, first, to match the geometric accuracy of the CORINE Land Cover data and second, to compare the calculation results acquired with the higher-resolution rasterized BDOT10k data. We found that it was possible to compare these methods and assess how much data resolution and selection of rasterization methods affected the calculations of the landscape metrics. The calculation results of specific fragmentation metrics differed tremendously when higher data resolutions and rasterization methods were used, especially when using the custom priority rasterization method.

Blood cardioplegia reduces intraoperative ventricular fibrillation and transfusion requirements compared to crystalloid cardioplegia in canine mitral valve repair.

Cardioplegic solutions are indispensable for open-heart surgeries, including mitral valve repair (MVR), a potentially curative treatment for myxomatous mitral valve disease in dogs. However, procedural methodologies are not fully established, and complications are yet to be comprehensively understood. Cardioplegic solutions contain various substances to protect the myocardium under temporal cardiac arrest. Nevertheless, ventricular fibrillation (VF) occurs as a common complication after releasing the crossclamp. Based on these backgrounds, the search for optimal cardioplegic solutions in dogs undergoing MVR is an urgent issue. This study aims to evaluate the occurrence of VF in dogs treated with blood cardioplegia (BCP) versus crystalloid cardioplegia (CCP) during MVR. A total of 251 client-owned dogs who underwent MVR from November 2015 to November 2017 were included. We retrospectively assessed the relationship between VF and type of cardioplegia (CCP or BCP) based on surgical records, including VF incidence, transfusion use, crossclamp time, and echocardiographic measurements. Logistic regression analysis showed that the CCP group was associated with the occurrence of VF (OR, 2.378; CI, 1.133-4.992; P = .022). In addition, the CCP group was associated with transfusion use (OR, 2.586; CI, 1.232-5.428, P = .022). There was no difference between the groups for the pre- and postoperative echocardiographic measurements. The BCP group had a lower incidence of VF and less transfusion use than the CCP group. This finding indicates that BCP may be a superior cardioplegic technique for MVR in dogs.

Evolutionary Multimodal Multiobjective Optimization for Traveling Salesman Problems

Multimodal multiobjective optimization problems (MMOPs) are commonly seen in real-world applications. Many evolutionary algorithms have been proposed to solve continuous MMOPs. However, little effort has been made to solve combinatorial (or discrete) MMOPs. Searching for equivalent Pareto optimal solutions in the discrete decision space is challenging. Moreover, the true Pareto optimal solutions of a combinatorial MMOP are usually difficult to know, which has limited the development of its optimizer. In this paper, we first propose a test problem generator for multimodal multiobjective traveling salesman problems (MMTSPs). It can readily generate MMTSPs with known Pareto optimal solutions. Then we propose a novel evolutionary algorithm to solve MMTSPs. In our proposed algorithm, we develop two new edge assembly crossover operators, which are specialized in searching for superior solutions to MMTSPs. Moreover, the proposed algorithm uses a new environmental selection operator to maintain a good balance between the objective space diversity and decision space diversity. We compare our algorithm with five state-of-the-art designs. Experimental results convincingly show that our algorithm is powerful in solving MMTSPs.

Пошук оптимальних складів фібробетонів жорсткого дорожнього покриття з використанням експериментально-статистичних моделей

The experiment was conducted according to a 15-point plan. In the experiment, the following three factors of the composition of rigid pavement fiber-reinforced concrete varied: the amount of Portland cement from 290 to 350 kg/m3, the amount of basaltic fiber from 0.9 to 1.5 kg/m3, the amount of superplasticizer from 0.6 to 1% of the cement mass. A set of experimental statistical models was obtained. They describe the influence of factors on compressive strength, flexural strength, frost resistance, abrasion resistance and cost of fiber-reinforced concrete. The selection of optimal compositions of fiber-reinforced concrete rigid pavement for the roads of II-III and Ib categories was carried out using these models. The selection of optimal compositions of fiber-reinforced concrete was carried out graphically using square diagrams. The method of representing the factor space as 7 "square" type diagrams was used when fixing the amount of cement in the composition from 290 to 350 kg/m3 with a step of 10 kg/m3. This discretization allows for a clearer and more accurate search for optimal solutions. The levels of compressive strength, flexural strength and frost resistance were used as limitation criteria. These levels meet the requirements of DBN B.2.3-4:2015 for rigid pavement materials of the corresponding category. The concrete cost index was used as an optimization criterion. The abrasion resistance of concrete was also controlled to guarantee a correct choice. Two compositions of fiber-reinforced concrete for the roads of the II and III categories were chosen. These compositions have a compressive strength of 43 and 45 MPa, a flexural strength of 5 and 5.1 MPa, abrasion of about 0.36 g/cm2 and frost resistance of F200. Two compositions of fiber-reinforced concrete for category Ib roads were also selected. These compositions have a compressive strength of 48 and 50 MPa, a flexural strength of 5.5 MPa, abrasion of about 0.35 g/cm2 and frost resistance of F200. The compositions have chosen the lowest cost price while ensuring the strength and durability of the material. The use of this fiber-reinforced concrete makes it possible to increase the intervals between repairs during the maintenance of cement concrete roads in typical climatic conditions in Ukraine.

Artificial intelligence‐enabled smart city management using multi‐objective optimization strategies

AbstractThis article outlines an integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi‐Objective Possibilistic Linear Programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades‐long development of classical optimisation techniques. As a result of its potential for use in the real world, multi‐objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi‐objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non‐linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision‐making in real‐world optimization scenarios.

Optimal Re-Materialization Strategies for Heterogeneous Chains: How to Train Deep Neural Networks with Limited Memory

Training in Feed Forward Deep Neural Networks is a memory-intensive operation which is usually performed on GPUs with limited memory capacities. This may force data scientists to limit the depth of the models or the resolution of the input data if data does not fit in the GPU memory. The re-materialization technique, whose idea comes from the checkpointing strategies developed in the Automatic Differentiation literature, allows data scientists to limit the memory requirements related to the storage of intermediate data (activations), at the cost of an increase in the computational cost. This paper introduces a new strategy of re-materialization of activations that significantly reduces memory usage. It consists in selecting which activations are saved and which activations are deleted during the forward phase, and then recomputing the deleted activations when they are needed during the backward phase. We propose an original computation model that combines two types of activation savings: either only storing the layer inputs, or recording the complete history of operations that produced the outputs. This paper focuses on the fully heterogeneous case, where the computation time and the memory requirement of each layer is different. We prove that finding the optimal solution is NP-hard and that classical techniques from Automatic Differentiation literature do not apply. Moreover, the classical assumption of memory persistence of materialized activations, used to simplify the search of optimal solutions, does not hold anymore. Thus, we propose a weak memory persistence property and provide a Dynamic Program to compute the optimal sequence of computations. This algorithm is made available through the Rotor software, a PyTorch plug-in dealing with any network consisting of a sequence of layers, each of them having an arbitrarily complex structure. Through extensive experiments, we show that our implementation consistently outperforms existing re-materialization approaches for a large class of networks, image sizes and batch sizes.

Integrated Approach to Diagnosis and Treatment in Clinical Medicine: Search for Optimal Solutions

Aims: This review examines the Current State of the Integrated Healthcare approach and assesses the impact of integrated care models on patient health outcomes. Methodology: A systematic review using PubMed database and Google Scholar database. Using period 2019-2023 and keywords Clinical Medicine AND Diagnosis AND Treatment AND Integrated Approach and "integrated care" AND "diagnosis and treatment" AND "clinical medicine" AND "multidisciplinary care" were employed to identify relevant studies from PubMed and Google Scholar. Finally, 18 articles were sampled. Results: The systematic literature review on integrated diagnosis and therapy in clinical medicine shows many study designs and sample sizes. RCTs, the gold standard, are included in the review, along with Prospective Cohort Studies, Retrospective Studies, Cross-Sectional Studies, and Post-hoc Analyses. Some studies have large populations and rich datasets, whereas others have smaller samples. These studies provide varied approaches and timeframes for applying the integrated approach in clinical practice and discuss integrated healthcare approaches that use multidisciplinary teams, innovative technologies, and targeted interventions to treat chronic disorders and multi-morbidities. Integrated healthcare enhances patient outcomes, adherence to recommendations, and quality of life, emphasizing the need for tailored care in modern medicine. Scientific Novelty: Exploring a complex, integrated clinical medicine diagnosis and treatment approach to find patient care solutions. Conclusion: Clinical medicine's integrated diagnosis and treatment strategy is dynamic and promising. It can improve patient care, lower healthcare costs, and improve the quality of life for people with diverse health issues.

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative and competitive multi-objective optimization method, which we refer to as LCCMO. We begin by presenting a multi-rule cooperative initialization approach to create a population that combines strong convergence and diversity. This diverse population forms the foundation for our optimization process. Next, we develop a multi-level cooperative global search strategy that explores effective genes within solutions from different angles and sub-problems. This approach enhances our search for optimal solutions. Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.

A linearly decreasing deterministic annealing algorithm for the multi-vehicle dial-a-ride problem.

Dial a ride problem (DARP) is a complex version of the pick-up and delivery problem with many practical applications in the field of transportation. This study proposes an enhanced deterministic annealing algorithm for the solution of large-scale multi-vehicle DARPs. The proposed method always explores the feasible search space; therefore, a feasible solution is guaranteed at any point of termination. This method utilises advanced local search operators to accelerate the search for optimal solutions and it relies on a linearly decreasing deterministic annealing schedule to limit poor jumps during the course of search. This study puts forward a systematic series of experiments to compare the performance of solution methods from various angles. The proposed method is compared with the most efficient methods reported in the literature i.e., the Adaptive Large Neighbourhood Search (ALNS), Evolutionary Local Search (ELS), and Deterministic Annealing (DA) using standard benchmarks. The results suggest that the proposed algorithm is on average faster than the state-of-the-art algorithms in reaching competitive objective values across the range of benchmarks.

Solving real-world optimization tasks using physics-informed neural computing

Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario development. Recently, machine learning applications, including deep reinforcement learning (RL) and genetic algorithms (GA), have emerged in these real-world optimization tasks. We introduce a new machine learning-based optimization scheme that incorporates physics with the operational objectives. This physics-informed neural network (PINN) could find the optimal path in well-defined systems with less exploration and also was capable of obtaining narrow and unstable solutions that have been challenging with bottom-up approaches like RL or GA. Through an objective function that integrates governing laws, constraints, and goals, PINN enables top-down searches for optimal solutions. In this study, we showcase the PINN applications to various optimization tasks, ranging from inverting a pendulum, determining the shortest-time path, to finding the swingby trajectory. Through this, we discuss how PINN can be applied in the tasks with different characteristics.

Linear constraints on variables in influence diagrams for causal models

The paper considers some ways of representing probabilistic causal models using Bayesian network theory (hereafter referred to as BN). These models describe well problems with different types of uncertainty. The theory of BN extended with some additional types of nodes called influence diagrams or IDs. Influence diagrams make it possible to consider a number of solution options, to evaluate them quantitatively, and to select the best of the considered options. However, it is practically impossible to find an optimal solution in an ID. It is not even possible to create a system of linear constraints on some variables in an ID, although there is a large class of practical problems with such constraints. The paper describes the idea of extending ID to describe linear constraints on some variables of the BN. In the future, it will help to use the ideas of linear programming in ID to find an optimal solution in the sense of LP for problems with different types of uncertainties and causal relationships between some variables. This work has been done under grant AP19679142 "Search for optimal solutions in Bayesian networks in models with linear constraints and linear functionals. Development of algorithms and programs " (2023-2025) of MES RK. This project will develop the theory for finding optimal solutions in Bayesian networks. Optimality will be understood in the sense of linear programming - a system of linear constraints, extremum of a linear functional. The theory will be implemented in a software product.

Multi-use of small performance halls – case studies of modern cultural facilities in Poland

Contemporary architecture is expected to meet not only functional goals and high aesthetic quality, but also to take into account changing social needs, which is in line with the trend towards sustainable building. It is therefore important to consider the idea of multifunctionality when implementing new developments, especially in small towns. The subject of this paper is the analysis of three buildings in Wieliczka, Dąbrowa and Pszczyna, in which the aspect of multifunctionality was considered differently. At the same time, the discussed examples will benefit researchers, investors and designers alike, as they present specific architectural aesthetic and technological solutions related to the realization of small auditoriums – a subject that is rarely dealt with in scientific publications. The main aim of this paper was to draw attention to the need for an individual approach to the design process and a multi-criteria search for optimal design solutions for the hall, moving away from standardization. Finally, the realization of small multifunctional performance space fits in with the idea of sustainable development, which is based on combining the needs of the present generation with the ability to meet the needs of future generations.

Використання комп’ютерних технологій в епоху цифрової економіки

The article is devoted to the assessment of the use of computer technologies in the economy. The impact of digital technologies on the development of domestic companies is described, changes in economic indicators are evaluated, taking into account the implementation of digital economy solutions. The article discusses the use of computer technologies in the agricultural sector, the medical industry and the sphere of State regulation. Real examples of the introduction of automation and robotization at large production enterprises and their positive impact on the economic condition of the company, in compliance with environmental norms and standards of Industry 5.0 are presented. When considering the agrarian industry, progressive strategies for the use of artificial intelligence, Big Data technologies, remote process control are allocated, which increased competitiveness and contributed to the transition of companies from the local market to the international one. In the course of a heuristic study of the medical field, it was found that robotization affected the improvement of economic indicators, which was caused by an increase in the efficiency and quality of production of medicines with the international GMP standard. It is found that the computerization of document management and time management systems contributed to improving the level of trust in public medical services provided by doctors. The analysis of statistical data has shown that the number of enterprises using digital technologies in the management and sale of goods is increasing, so the introduction of automation and robotization is necessary to improve the performance of enterprises. Analysis of the volume of digital trade in monetary terms showed a significant increase over the studied period of time due to the introduction of computer technologies. The article states that digitalization and automation have a positive impact on the economic activity of companies not only at the micro level, since the digitalization strategies of companies can be transferred to the macro level and applied in all spheres of life. Prospects for further research include an analysis of the economic impact of digitalization, the search for optimal solutions for automating processes while reducing the cost of technology.