Today, the field ofbetting and bookmaking is popular with a wide range of sports fans. Issues of predicting the outcome of future events are and will be relevant for everyday life, sports, politics, etc. With the increasing number and quality of methods of intellectual analysis, the idea of predicting the results of sporting events became feasible. Applying different mathematical methods helps to obtain more accurate predictions of results than subjective expert estimates. The paper introduces the concept of betting and describes in general terms the task of bookmaking. The purpose of the study and the tasks that must be accomplished to achieve the goal are identified. Existing research results of different scientists who have researched this problem are analyzed. There arefour basic principles for predicting the outcome of sports events. Different approaches to the task have been considered and our own way of solving it has been proposed. Methods such as Poisson distribution, simulation modeling of the Markov Monte Carlo chain, and many other research methods have been considered. The formulation of the problem is formulated and the properties of the problem are investigated. A backtesting algorithm was developed and described as a mechanism for presenting team statistics at any point in time for a particular season to collect sports event data. Correlation analysis for the selected parameters was shown to show a moderate correlation of data and the use of Google AutoML to identify patterns between the data was described. The importance of using machine learning to solve this problem is outlined. A system has been developed that collects event data and calculates statistics for each team at each point of time using the backtesting algorithm. A service has been developed to create and test the quality of the strategy. The results of experimental studies of task efficiency are presented, where we conducted experimental sets of strategies with and without adding the result of the AutoML service and for each strategy the Pearson correlation coefficient was calculated based on the results of two past seasons. The results obtained are analyzed.

УДК 519.854.2 поЛіноміаЛьна СкЛадоВа пдС-аЛГоритму роЗВ'яЗання однієї ЗадаЧі теорії роЗкЛадіВ Розглянуто властивості задачі календарного планування виконання завдань із загальним директивним терміном ідентичними паралельними приладами за критерієм максимізації моменту запуску приладів за умови, що усі завдання не запізнюються. Застосовуючи методологію побудови ПДС-алгоритмів на основі ознак оптимальності розкладів визначена множина перестановок, що дозволяють послідовно покращувати значення критерію. Ці перестановки покладені в основу розробленої ПДС-складової алгоритму розв'язання задачі.

The work is devoted to the multiobjective task of scheduling, in which a given set of works must be performed by several performers of different productivity. A certain number of bonuses is accrued for the work performed by the respective executor, which depends on the time of work performance. The criteria for evaluating the schedule are the total time of all jobs and the amount of bonuses spent. In the research the main approaches to solving multiobjective optimization problems were analyzed, based on which the Pareto approach was chosen. The genetic algorithm was chosen as the algorithm. The purpose of this work is to increase the efficiency of solving multicriteria optimization problems by implementing a heuristic algorithm and increase its speed. The tasks of the work are to determine the advantages and disadvantages of the approaches used to solve multicriteria optimization problems, to develop a genetic algorithm for solving the multicriteria scheduling problem and to study its efficiency. Operators of the genetic algorithm have been developed, which take into account the peculiarities of the researched problem and allow to obtain Pareto solutions in the process of work. Due to the introduction of parallel calculations in the implementation of the genetic algorithm, it was possible to increase its speed compared to the conventional version. The developed algorithm can be used in solving the problem of optimal allocation of resources, which is part of the system of accrual of bonuses to employees.

The problem of abuse by community of free-ride users in decentralized networks with reward distribution is considered. Various structures for organizing nodes of a decentralized network belonging to the community of free-ride users, which can be used to obtain the greatest benefit in reward distributing in the network, are proposed. To compare the proposed structures, simulation of reward distribution was used. After analyzing the results of experiments, it was concluded that combining the nodes belonging to community of free-ride users into a structure of the "Generalized Ring" type with a sufficiently large number of nodes gives the best results from all the proposed structures.

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