оптимізація

Optimization of Business Processes in the Pharmaceutical Industry in the Conditions of the Circular Economy

The article formulates the advantages and challenges of implementing circular economy princi- ples in the pharmaceutical industry. The main strategic aspects of optimizing business processes in the context of sustainable development are substantiated. The results of the study highlight the specifics of implementing circular economy principles in the pharmaceutical industry by updating the environmen- tal, economic and social benefits of such a transition, as well as analyzing potential opportunities and challenges along the way.

Digital Tools in the Energy Drink Market

In the current context of digital transformation within the energy drinks market, the use of digital technologies has become a crucial tool for enhancing the efficiency of business processes, marketing strat- egies, and consumer engagement. However, despite considerable opportunities, the widespread imple- mentation of digital instruments in this sector faces several challenges that require both academic analysis and practical solutions. One of the key issues is the adaptation of energy drink producers' business models to the realities of the digital environment.

Optimization of Human Resource Management Using Business Analytics

The article explores the administration of human resource potential using the case study of LLC ‘Trade House “Galka”. It examines the theoretical foundations of human resource management, key ad- ministration methods, and major factors influencing the efficiency of personnel policies within the com- pany. A general analysis of the company’s production and economic activities is conducted, identifying strengths and weaknesses in the existing human resource management system.

OPTIMIZATION OF THE ROUTING PROCESS IN DISTRIBUTED NETWORKS USING MACHINE LEARNING

The article proposes an innovative approach to optimize the routing process in distributed networks using machine learning techniques, specifically reinforcement learning. This method enables the adaptive determination of optimal data transmission paths based on current network conditions, enhancing overall performance and resilience to dynamic traffic fluctuations. The proposed approach dynamically adjusts to variations in network topology, traffic load, and node availability, ensuring efficient data flow management even in highly dynamic environments.

HYBRID MODEL OF NETWORK ANOMALIES DETECTION USING MACHINE LEARNING

The increasing complexity of cyber threats requires the development of effective methods for detecting and classifying attacks in network traffic. This study analyzes the effectiveness of three popular machine learning algorithms: Random Forest, which is used for anomaly detection, Support Vector Machines (SVM), which performs cyber threat classification, and autoencoders, which are used for data preprocessing and deep traffic analysis.

DISCRETE APPROXIMATION IN THE PROBLEMS OF PLACING VECTOR GRAPHIC OBJECTS ON A PLANE

This article presents a new approach to finding possible placements of vector objects on a plane using discrete approximation. The proposed method significantly reduces the computational complexity of the problem by converting vector images into a discrete form represented by a pixel grid. This enables faster intersection checks between objects through the analysis of occupied grid elements, thus simplifying the process of modeling graphic placement.

Mathematical Simulation of Nanofiltration Process: State of Art Review

A review of publications devoted to the mathematical simulation of the nanofiltration process was carried out, the advantages, limitations, and areas of application of various modeling approaches were determined. It was found that the most effective approaches are based on the extended Nernst-Planck equation, Donnan equilibrium, as well as methods of computational fluid dynamics and molecular dynamics. The use of software for solving nanofiltration simulation problems was considered.

Optimization Algorithms for Wireless Sensor Networks to Solve Maximization Problems

The paper describes a constant time clustering algorithm that can be applied on wireless sensor networks. The scheme for rate control, scheduling, routing, and power control protocol for wireless sensor networks based on compressive sensing has been shown. Using network utility maximization formulations, cross-optimization solutions using Lagrangian multipliers in network access control and physical layers have been presented. The optimization solutions have been developed by solving the optimization model of network utility maximization.

Multi-agent modeling of traffic organization in urban agglomerations

The authors consider the features of multi-agent modeling for traffic optimization in the central areas of cities. While evaluating the unique challenges associated with the high concentration of vehicles, pedestrians and historical buildings, the potential of multi-agent systems to effectively solve the problem of congestion, safety and quality of life in urban areas is investigated. The potential of multi-agent modeling in the context of traffic management in the central areas of the city allows us to identify the key challenges and opportunities.

OPTIMIZATION OF GEOMETRY OF PIEZORESISTIVE EFFECT ON THE EXAMPLE OF CUBIC CRYSTALS

On the example of semiconductor crystals Ge, Si, PbTe, PbS, InSb with different levels of doping and different types of conductivity, the geometry of the piezoresistive effect was optimized, namely, such directions of voltage measuring and uniaxial pressure applying were determined, which ensure the maximum achievable value of the effect. The optimization is based on an approach using the construction and analysis of extreme surfaces that represent all possible maxima of the objective function (the magnitude of the effect) under different spatial orientations of interacting factors.