Finding Optimal Solutions Research Articles

Аналіз проблем і математичних методів для їх вирішення в транспортній логістиці

The article examines the importance of transportation for a country's economic development in a globalized world. Transportation is the foundation of supply chains, ensuring the efficient movement of goods, materials, and people, which contributes to global trade, economic growth, cultural exchange, and social integration. The efficiency of transportation technologies is a key factor in successful logistics, where mathematical methods play an important role. The aim of the article is to analyze contemporary issues in transportation logistics and the application of mathematical methods to solve them. The paper addresses issues such as route optimization, supply chain modeling, demand forecasting, the implementation of innovations in transportation technologies, efficient warehousing, and inventory management. Route optimization is critically important for reducing transportation costs and increasing transportation efficiency, which directly impacts economic indicators. Supply chain modeling helps to understand and improve the interaction between different participants in the logistics process, contributing to the creation of more resilient and adaptive supply systems. Demand forecasting is necessary for resource planning and avoiding shortages or surpluses of goods. The implementation of innovations in transportation technologies, such as automation and the use of drones, opens new opportunities for enhancing the efficiency of logistics processes. The application of linear and dynamic programming methods allows for finding optimal solutions under complex conditions of uncertainty. Simulation modeling helps study various scenarios and their impact on logistics processes. Heuristic methods and genetic algorithms are powerful tools for finding approximate solutions in cases where exact methods are inefficient. Machine learning methods enable the processing of large volumes of data and making predictions based on historical data, significantly improving the accuracy of managerial decisions. The article's conclusions emphasize the need for a comprehensive approach to solving transportation logistics problems, which includes the integration of the latest mathematical methods and innovative technologies to achieve high efficiency and competitiveness in the modern conditions of globalization.

System analysis of the procurement warehouse for the achievement of its effectiveness

The work shows the features of applying system analysis to the problems of increasing the efficiency of the procurement section of a large machine-building work. The purpose of the work is to eliminate obstacles to achieving the efficiency of the work of the procurement site with the help of system analysis. Specific solutions are presented for a comprehensive increase in the interaction of subsystems of the procurement site, redevelopment, and the use of more advanced devices to increase efficiency. To solve the problems, the main tools of system analysis are applied - suprasystem-system-subsystem analysis, structural analysis, functional analysis, and site-specific calculations - such as analysis of norms and time of operations, analysis of layouts and compliance with the organizational structure, as well as analysis of equipment operation and finding optimal solutions for its equipment. Discussed the results of the system analyses and limitation of it's implementation in real workshop.

ШТУЧНИЙ ІНТЕЛЕКТ: МОЖЛИВОСТІ І ВИКЛИКИ ДЛЯ РЕСТОРАННОГО БІЗНЕСУ

Revolutionary changes associated with the development of information technologies and the transition to Industry 4.0 are gradually covering all spheres of the economy and social life. The focus is on artificial intelligence, which actually allows computers to "understand" and "think" like the human brain and perform tasks that normally require human intelligence. The relevance of the study of artificial intelligence technologies in the activities of restaurant business enterprises is substantiated in the article. Artificial intelligence in the restaurant business is a valuable technology that allows to optimize most processes and improve the experience of visitors. Interest in this innovation grew significantly during the COVID-19 pandemic, when restaurateurs, in order to stay on the market, began to implement technical solutions in a matter of months, which would otherwise take years to implement. The directions of application of AI algorithms in the restaurant business according to the spheres of activity of enterprises are systematized. This includes, in particular: procurement of ingredients, production, sales, marketing, personnel, finance, organization and management. Based on the study of AI algorithms in the restaurant business, the main trends that will characterize the restaurant of the future are determined. Among them: interaction with a system of virtual personal assistants; dynamic menu; cloud kitchens and virtual restaurants; dependence on delivery services; food as an accompanying service of non-food company offers; the change in eating behavior on the road caused by the development of autonomous cars; self-driving cars for food delivery; software and recipes for automated cooking – digital intellectual property; recipes generated by AI; automation and robotics; restaurant – a place for communication; personalized service and loyalty system. The benefits of artificial intelligence for the restaurant business are outlined. Among them are improving work efficiency, reducing costs and optimizing resources; expanding the range of clients; improving the quality and personalization of the services provided; new inventions; increasing the speed of finding optimal solutions; impartiality, avoiding errors and reducing the human factor. The challenges accompanying the use of AI technology in the practical activities of enterprises are defined.

Взаимосвязь психических процессов с технической подготовленностью у баскетболисток 14–15 лет

For effective competitive activity in youth basketball, it is necessary to take into account both external and internal factors, since they influence the quality of technical actions in situations of confrontation with an opponent. Features of feeling and perception, as well as the speed of processing information coming from the outside, are closely related to the issues of players finding optimal solutions, which means that the coach needs to properly influence them. The athlete’s readiness for professional activity at the level of mental processes (switching attention, error correction, perception of information, etc.) becomes increasingly important and determines the relevance of this study. The research aims to reveal correlations between the technical preparedness and mental processes of basketball players aged 14–15 in the pre-competition period of training. The research objective is to determine the level of interrelations between technical preparedness and the nature of the course of mental processes of basketball players aged 14–15 based on a correlation analysis of data using the nonparametric Spearman criterion. The level of technical readiness was determined by the indicator of the shuttle run completion time, dribbling for 20 meters, figure-eight dribbling for 20 meters, dribbling and shooting, execution time and accuracy of hitting shots from medium and long distances, and free throws. Mental processes were assessed according to the data of the type of information perception (visual, kinesthetic, audial), motor response rates, estimates of time intervals, memory capacity, switching, and attention selectivity. The work was carried out in October 2020 and involved 16 female basketball players aged 14–15. The results of the correlation analysis allowed us to establish the presence of triple, double, and single connections between mental processes and indicators of technical preparedness. The largest number of connections was revealed in the following indicators: attention switching, individual minute, number of throws in motion, and long throw. The established direct and feedback links are described at high and medium levels of significance. The presence of numerous links between the indicators of technical preparedness and mental processes associated with the perception and processing of information at high and medium levels indicates the need for targeted training of basketball players aged 14–15 to teach the ability to manage sensory-perceptual processes, distinctive sensitivity, modality of information perception, operational thinking, and attention.

Parametric optimization of a multi-output electromagnetic mechatron module

The article is devoted to the problem of determining the optimal dimensions of the design of reciprocating linear electromagnetic engine. As an optimization criterion, the parameters of the mechatron module based on the multi-output linear electromagnetic motor are determined by means of a constructive calculation scheme and its variable parameters are determined by the method of non-linear programming, the geometric dimensions are simplified, the mass of the electromagnetic motor body, the mass of the motor armature and the masses of the motor stator and the restrictions on their dimensions are determined the issues of finding optimal solutions are considered. Optimization of mechatronic modules based on multi-output linear electromagnetic motors on various parameters leads to increase in dynamic performance of the executive organ, saving energy consumption, and allows to improve performance and service quality. Also, based on the analysis of the existing electromagnetic linear motors, the structural scheme of the mechatron module based on the multi-output linear electromagnetic engines, which allows to perform step movements according to spatial coordinates with high accuracy and speed, is presented. The algorithmic basis of computing is based on the weight and size characteristics of execution mechanisms, and it has been proposed to allow solving optimal design tasks of execution mechanisms taking into account zvenolar lengths, displacements, speeds and accelerations during movement, as well as creating robotic systems with high performance in service. This structure serves to ensure the interaction of the executive body with the object in the links of industrial robots.

МОДЕЛЮВАННЯ ЗАДАЧ ОПТИМІЗАЦІЇ УПРАВЛІННЯ ІТ-ПРОЕКТАМИ

Abstract. The article focuses on exploring mathematical models for optimizing business processes in project management within IT companies in the context of contemporary trends and challenges. Project management involves the process of guiding and coordinating people's activities, as well as managing material and financial resources throughout the project's lifecycle. This process encompasses applying modern management methods and techniques to achieve project goals. The dynamic allocation modeling of IT projects among teams to ensure high-quality development at maximum efficiency with minimal costs is a pertinent task in IT project management. The article's objective is to select a mathematical framework for decision-making regarding the optimal IT project management program to enhance the efficiency of each IT team and the entire IT company. The research involved an analysis of scientific literature on modeling project management in IT companies. The article proposes focusing on developing dynamic multi-criteria optimization models for human resource management, emphasizing the distribution of IT projects among executing teams. The research aims to elevate the strategic and operational levels of IT personnel management to achieve optimal outcomes in information technology. The analysis concludes that despite the advantages of various mathematical modeling methods, none of them in isolation allows for constructing a sufficiently detailed mathematical model. Methods and models addressing IT enterprise modeling problems may oversimplify the issue or, conversely, overly complicate it, complicating the selection of input data. The functioning of the IT enterprise was analyzed through the lens of external and internal indicators concerning the solved task. A verbal model of IT project distribution among teams, considering the time factor, was developed. This model formulated a system of basic concepts and ideas, allowing the dynamic formulation of the problem using widely accepted linear optimization models. This will facilitate finding optimal solutions for IT project distribution tasks over a specific period. Future research should focus on selecting a solution method for the model and implementing the constructed mathematical model using appropriate tools. The research findings presented can serve as a basis for further academic exploration and practical application in the field of IT project management. Keywords: modeling, optimization, business processes, project management, labor resources, IT projects.

Modelling smart energy consumption with hybrid demand management in off-grid electrical system considering techno-economic indices

This study proposes day-ahead power scheduling for electrical systems in off-grid mode, emphasizing consumer involvement. Bi-Demand Side Management (DSM) approaches like strategic conversion and demand shifting are proposed for consumer involvement. Multiple objectives are modelled to voltage profile improvement and reduce the operation energy cost. The non-dominated solutions of the voltage of buses and operation energy cost are generated by enhanced epsilon-constraint technique, simultaneously. The General Algebraic Modeling System (GAMS) software is proposed for solving optimization problems. A combination of decision-making methods like weight sum and fuzzy procedures are implemented for finding optimal solution non-dominated solutions. The proposed method’s effectiveness is confirmed through numerical simulations carried out on several case studies that utilize the 33-bus electrical system. The findings illustrate the substantial effectiveness of demand-side participation in improving power dispatch and the optimal rate of multiple objectives. By using DSM, operation cost is reduced by 21.58% and the voltage index is improved by 13.36% than the lack of implementing DSM.

Streamlining logistics flows with lean tools using TX Plant Simulation software support

A critical factor in the manufacturing industry is waste and losses in individual operations and processes. The expansion of elements of lean production, such as the pull system with the interconnection of key technologies, brings new elements of production planning and control. Thanks to new technologies, many more possibilities exist to uncover potential bottlenecks. Simulations and modelling bring new possibilities for experimentation in the virtual environment of prepared systems or devices without directly disturbing the functioning system. The presented article deals with the issue of applying lean approaches in finding optimal solutions within logistics flows. The aim of the article is to define and test the potential for streamlining logistics flows with TX Plant Simulation software support. The research and empirical part of the study was solved based on a rigorous analysis of the initial situation in a specific company for the selected product group, as the Value Stream Mapping method was used for the analysis. A simulation model of a real line was created in the TX Plant Simulation software with the help of the Value Stream Mapping library for value creation, which is part of the software. The goal was to find out possible waste within the logistics flow, and its causes and propose the necessary steps to eliminate the waste.

The Role of Mathematics in Artificial Intelligence and Machine Learning

Mathematics serves as the foundational backbone of “artificial intelligence (AI) and machine learning (ML), providing the essential” tools and frameworks for developing sophisticated algorithms and models. the pivotal role of various mathematical disciplines, including linear algebra, calculus, probability theory, and optimization, in advancing AI and ML technologies. We begin by examining how linear algebra facilitates the manipulation and transformation of high-dimensional data, which is crucial for “techniques such as principal component analysis (PCA) and singular value decomposition (SVD)”. Next, we delve into the applications of calculus in training neural networks through gradient-based optimization methods, highlighting the importance of differentiation and integration in backpropagation and loss function minimization. the role of probability theory in handling uncertainty and making predictions, emphasizing its application in Bayesian networks, Markov decision processes, and probabilistic graphical models. Additionally, we discuss optimization techniques, both convex and non-convex, that are fundamental to finding optimal solutions in machine learning tasks, including support vector machines (SVMs) and deep learning architectures.

Методика выбора алгоритмов оптимизации живучести энергетических инфраструктур

The article considers one of the most difficult tasks of studying the resilience of energy infrastructures – finding effective combinations of measures to increase resilience. To solve this problem, the article describes an approach that considers it as a problem of structural-parametric optimization of energy infrastructures, which is built according to a two- or three-level scheme. The approach described in the article adds another layer to the middle of the above scheme, which checks the efficiency of the selected equipment under extreme conditions created by a given set of large disturbances. The main disadvantage that the approach inherits from the structural-parametric optimization of energy infrastructures is a high computational complexity of the multilevel optimization scheme. However, the unacceptable calculation time can be explained by the selection of inappropriate optimization algorithms. In the papers concerning the structural-parametric optimization of energy infrastructures publ in the literature, the question of comparing optimization algorithms with each other is clearly not raised. Therefore, this article proposes a three-stage methodology for selecting optimization algorithms, according to which, before solving a specific problem of optimizing the resilience of energy infrastructures, first test the algorithms, and then choose the best one based on a multi-criteria analysis of the test results. To apply the methodology, it is necessary to develop a special lightweight version of the task of optimizing resilience and prepare a testbed for organizing and conducting test computational experiments. The application of the methodology is demonstrated by the example of choosing heuristic methods for finding optimal solutions from the PaGMO library used at the external level of the resilience optimization scheme of the Unified Gas Supply System of Russia. In total, five popular evolutionary algorithms were tested, the most suitable of which turned out to be a genetic sorting algorithm without NSGA-II dominance.

A Data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline Data-Driven Dynamic Optimization.

Existing work on offline data-driven optimization mainly focuses on problems in static environments, and little attention has been paid to problems in dynamic environments. Offline data-driven optimization in dynamic environments is a challenging problem because the distribution of collected data varies over time, requiring surrogate models and optimal solutions tracking with time. This paper proposes a knowledge-transfer-based data-driven optimization algorithm to address these issues. First, an ensemble learning method is adopted to train surrogate models to leverage the knowledge of data in historical environments as well as adapt to new environments. Specifically, given data in a new environment, a model is constructed with the new data, and the preserved models of historical environments are further trained with the new data. Then, these models are considered to be base learners and combined as an ensemble surrogate model. After that, all base learners and the ensemble surrogate model are simultaneously optimized in a multitask environment for finding optimal solutions for real fitness functions. In this way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the ensemble model is the most accurate surrogate, we assign more individuals to the ensemble surrogate than its base learners. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art offline data-driven optimization algorithms. Code is available at https://github.com/Peacefulyang/DSE_MFS.git.

Scheduling of Software Test to Minimize the Total Completion Time

This paper investigates a single-machine scheduling problem of a software test with shared common setup operations. Each job has a corresponding set of setup operations, and the job cannot be executed unless its setups are completed. If two jobs have the same supporting setups, the common setups are performed only once. No preemption of any processing is allowed. This problem is known to be computationally intractable. In this study, we propose sequence-based and position-based integer programming models and a branch-and-bound algorithm for finding optimal solutions. We also propose an ant colony optimization algorithm for finding approximate solutions, which will be used as the initial upper bound of the branch-and-bound algorithm. The computational experiments are designed and conducted to numerically appraise all of the proposed methods.

Comparing the Effects of Linear and Nonlinear Pedagogies on Kinematic Variables During a Landing Task Related to Anterior Cruciate Ligament injury

Background and Aims In recent years, the use of motor learning training methods to reduce anterior cruciate ligament (ACL) injuries has been considered by researchers, and finding the best training method in this field has become very important. In this study, we aim to compare the effects of linear and non-linear motor learning methods on kinematic variables during a landing task. Methods in this study, 24 students from Kharazmi University who were beginner basketball players, were selected by a convenience sampling method, and divided into two groups of linear pedagogy (LP) and non-linear pedagogy (NLP). They received training methods for two months, two sessions per week (each for 90-115 minutes). In the LP group, the presentation of movement patterns and feedback was used. In the NLP group, the manipulation of constraints (environmental factors and tasks) was used so that the individuals could discover their own unique performance. The measured kinematic variables were trunk flexion angle, hip flexion angle, knee flexion angle, knee valgus angle, ankle dorsiflexion angle, hip range of motion, knee range of motion, ankle range of motion, hip flexion range, and knee flexion range. Results The results of the analysis of covariance showed that the NLP had significantly better effects on all kinematic variables compared to the LP (P<0.05). Conclusion The NLP, by focusing on exploration and finding optimal solutions, is a more effective strategy to prevent ACL injury by affecting kinematic variables. This emphasizes the role of constraint manipulation in training.

Accurate parameter identification of proton exchange membrane fuel cell models using different metaheuristic optimization algorithms

Recently, Numerous metaheuristic techniques have been utilized for the expedient identification of Proton Exchange Membrane Fuel Cells ‎ (PEMFCs) models. The reported techniques can inspect fickle in a wide search space for finding optimal solutions at the appropriate time. In this paper, recent optimization techniques are intended to better identify the unknown parameters of various PEMFCs. Three neoteric metaheuristic techniques of the Gazelle optimization algorithm (GOA), Prairie Dog Optimization Algorithm (PDO), and Reptile Search Algorithm (RSA) have been applied and evaluated. The proposed optimization algorithms have been validated for identifying the parameters of three PEMFCs‎: BCS 500 W PEMFC, SR-12 500 W PEMFC, and 250 W PEMFC stack‎. The sum of the squared errors (SSE) between the estimated voltage and the corresponding measured data was formulated as the objective function (OF). MATLAB/Simulink has been employed to validate the proposed optimization methods. The results showed that the three optimization techniques can solve the Fuel Cell ‎(FC) parameters identification optimization problem. Moreover, there are insignificant distinctions between the three applied methods with regard to their optimal value of the objective function. The finest technique considering the average value of the objective function is GOA for BCS 500 W-PEM with 0.0115, while the worst algorithm is PDO with 0.0112. Additionally, the statistical results prove that the three algorithms have 100%, 99.99%, and 100% tracking efficiencies for GOA, PDO, and RSA, respectively, according to 30 individual launches of BCS 500 W-PEM. The results have been evaluated via those of published articles. The I/V curves achieved employing GOA, PDO, and RSA methods provided a good agreement with the corresponding measured ones with the superiority of the GOA relating to the convergence speed, tracking efficiency, statistical metrics, and estimation accuracy.

Geometry optimization of steel formwork for steel–concrete composite slabs

The use of steel formwork incorporated into composite steel–concrete slabs stands out for contributing to sustainable development in civil construction. However, this construction system still has limited usage in Brazil, which may be due to the restricted availability of formwork geometries in the national market. As such, this study aims to determine the optimal steel geometry that results in the lowest steel consumption during manufacture of the composite slabs, taking into consideration the initial construction phase, in which the formwork is under the highest load demand. The Direct Strength Method (DSM) and the traditional Effective Width Method (EWM) are applied for designing the steel formwork during the construction phase, where the cold-formed steel profile resists the applied forces independently. For DSM, the critical buckling moments are determined using the elastic stability analysis program CUFSM, via Finite Strip Methods. The solution of the optimization problem was implemented in MATLAB® using Particle Swarm Optimization and considering two design variables: the thickness and the bend angle of the formwork web. In addition, the reliability of the proposed formulation was evaluated in the implementation phase of DSM and EWM. Numerical analyses are carried out on four different steel geometries currently available in the Brazilian market. The main results showed that the developed computational program was successful in finding optimal solutions for said geometries, as the optimization reduced steel consumption by an average of 21.6%. Thickness had a greater effect on the steel savings, and this variable decreases in the optimal solution, while the height and bend angle increase.

A method for design optimization of roof-integrated two-stage solar concentrators (TSSCs)

The optimal performance of two-stage solar concentrators (TSSCs) as building-integrated energy solutions is achievable through an integrative design of TSSCs and the building geometry. Such a design process reflects a multi-criteria decision-making problem involving different stakeholders having domain-specific concerns. To support this, in this study, a method to perform a two-step, design optimization of building and TSSCs engineered concerning building geometry is presented by developing a parametric model and applying, a multi-objective optimization algorithm (NSGA-II). The method allows for the creation of different design solutions in several iterations using a population set of individuals (decision variables) related to building design (i.e., roof type and slope, building orientation, length, width, and building type), and TSSC design (type, geometric ratio, mirrors' separation distance, modules number, and receiver area). Thus in several iterations, the method enables finding optimal solutions i.e., building with maximum direct normal irradiance (DNI) and TSSCs with maximum average load match index (av.LMI), and minimum average covered roof area (av.CRA). The application of the method is shown in a case study of four different, hypothetical residential buildings under the climate settings of Berlin. These buildings are illustrative (descriptive), which depict theoretical volumes as abstract representations of future constructions. Thus, a set of simulation runs are performed according to different model settings, to enable early-stage design exploration of future/new constructions which differ in size (varying floor levels) and energy demand. Results indicate that most solutions reflect buildings of high DNI (more than 125 kW/m2/month) and TSSC of av.LMI from 0.4 to 4.2, and av.CRA from 8% to 47%. Nevertheless, the performance of TSSCs depends on the building type. The method enables a performance-driven design approach, searches for the most feasible solutions, and helps to meaningfully support the decision-making process.

On efficiency and the Jain’s fairness index in integer assignment problems

Given two sets of objects, the integer assignment problem consists of assigning objects of one set to objects in the other set. Traditionally, the goal of this problem is to find an assignment that minimizes or maximizes a measure of efficiency, such as maximization of utility or minimization of cost. Lately, the interest in incorporating a measure of fairness in addition to efficiency has gained importance. This paper studies how to incorporate these two criteria in an integer assignment, using the Jain’s index as a measure of fairness. The original formulation of the assignment problem with this index involves a non-concave function, which renders a non-linear non-convex problem, usually hard to solve. To this aim, we develop two reformulations, where one is based on a quadratic objective function and the other one is based on integer second-order cone programming. We explore the performance of these reformulations in instances of real-world data derived from an application of assigning personnel to projects, and also in instances of randomly generated data. In terms of solution quality, all formulations prove to be effective in finding solutions capturing both efficiency and fairness criteria, with some slight differences depending on the type of instance. In terms of solving time, however, the performances of the formulations differ considerably. In particular, the integer quadratic approach proves to be much faster in finding optimal solutions.

Adaptive Capacity Task Offloading in Multi-Hop D2D-Based Social Industrial IoT

Traditional communication technologies such as cellular networks are facing problems to support high service quality when used for time-critical applications in an Industrial Internet-of-Things (IIoT) context, including real-time data transmission, route dependability, and scalability. To address these problems, device-to-device (D2D) communications based on social relationships can be used, which allow for task-offloading: resource-rich devices share unused computing resources with resource constraint devices. However, unbalanced task offloading in Social IIoT (SIIoT) might actually degrade the overall system performance, which is not desirable. In this paper, we propose an adaptive capacity task offloading solution for D2D-based social industrial IoT (ToSIIoT) which considers devices utilization ratio and strength of social relationships in order to improve resource utilization, increase QoS and achieve better task completion rate. The proposed approach consists of three aspects: social-aware relay selection in a multi-hop D2D communication context, choice of a resource-rich SIIoT device for task offloading, and adaptive redistribution of tasks. The paper proposes heuristic algorithms, as finding optimal solutions to the problems are NP-hard. Extensive experimental results show that the proposed ToSIIoT performs better than existing approaches in terms of utilization ratio, QoS violation, average execution delay, and task completion ratio.

On the Question of the Implementation of the Industrial Symbiosis Model in Timber Industry Complex of the Irkutsk Region

Since the middle of the last century and to this day, the economic development of countries is achieved by increasing industrial production, while causing great harm to the environment. In 2015, the 21st Conference of the Framework Convention on Climate Change adopted the Paris Agreement signed by 197 countries to reduce environmental impact by reducing greenhouse gases. Russia has also joined the policy of decarbonization of the national economy. The pulp and paper industry of the forestry complex of the Russian Federation is the main polluter of soil, air and water resources, which poses the problem of finding optimal solutions to reduce environmental impacts. The author presents the data of large timber enterprises in Eastern Siberia, which are the main environmental pollutants. The concept of industrial symbiosis is given, the prerequisites for the formation of industrial symbioses in the context of political, regional, socio-ecological and macroeconomic components are given. The author substantiates the introduction on the territory of the Irkutsk region of a special cluster form in the format of industrial eco-parks, which will allow the use of waste from enterprises as a subsequent resource in the activities of other enterprises. Thus, this recommendation will enable the Russian Federation to scale up the applied experience by introducing the proposed model to other industries.

CONTRIBUTION OF PHYSIOTHERAPY IN REDUCING THE RISK OF FALL IN THE ELDERLY

Nowadays, one of the major concerns of the specialists in the field of health is represented by finding optimal solutions to prevent falls among the elderly. The study was carried out on a group of 10 elderly people from a retirement home in Iasi, who were given a customized physiotherapy program, after being functionally evaluated using Tinetti walking test, Tinetti balance test and Romberg test. The research results highlight an improvement in balance and muscle control following specific physiotherapy programs. The conclusions of the current study highlight the fact that physiotherapy programs can reduce the risk of falls in the elderly by improving balance and walking abilities.