, 2022 (2 Vol 16) http://bijournal.hse.ru en-us Copyright 2022 Wed, 29 Jun 2022 11:51:12 +0300 Reduction of dimension in the problem of optimal management of a freight cars fleet using unmanned locomotives https://bijournal.hse.ru/en/2022--2 Vol 16/670739513.html       This paper considers the problem of optimal management of a fleet of freight cars by a transport railway operator. The solution to this problem is an optimal plan, which is a timetable for the movement of freight and empty railway cars, following which the transport operator will receive the maximum profit for the estimated period of time. This problem is reduced to the problem of linear programming of large dimension. Unlike the works of other authors on this topic, which mainly deal with methods of numerical solution of the corresponding linear programming problems, this article focuses on an algorithm that allows one to reduce their dimensionality. This can be achieved by excluding from the calculation those routes that obviously cannot be involved in the solution, or whose probability of participation in the final solution is estimated as extremely low. The effectiveness of the proposed modified algorithm was confirmed both on a model example (several stations, a short planning horizon) and on a real example (more than 1.000 stations, a long planning horizon). In the first case, there was a decrease in the dimension of the problem by 44%, while in the second – by 30 times. The development of a model for the personalized learning path using machine learning methods https://bijournal.hse.ru/en/2022--2 Vol 16/670746381.html       Today, the economy is undergoing a digital transformation. Its key barriers are a lack of qualified personnel, competencies and knowledge, as well as internal resistance in organizations. It can be overcome through quality staff development and training. An urgent problem is to build a personalized learning path. Modern research is aimed at the implementation of recommendation systems in order to select relevant material. However, these recommendations are based on digital traces; the student’s full personal profile, as well as organizational values are not considered. This study aims is to create an intelligent guide that would accompany an employee throughout his life in the organization, involving him in the learning process according to a personalized path based on a complex personal profile and reactions to educational material, training soft and hard skills in accordance with the values of the organization and the employee. Methods of system analysis, system engineering, psychodiagnostic research (the DISC model, Rowe’s “Decision-making Style” methodology, Honey and Mumford’s method of determining activity styles, psychotype test), software design and artificial intelligence (matrix factorization and neural networks) were used in this study. The study was conducted on a unique database collected as part of its implementation and consisting of educational tasks for soft skills development, plus data on their implementation by users with different soft skills profiles. An intelligent guide model has been developed and implemented as a software component for an enterprise management system. The basis consists of psychodiagnostic modules, organizational management, training and recommendations. The intelligence of the system we developed allows you to qualitatively form a personalized learning path that will involve an employee not only in the learning and development process, but also in achieving organizational goals. The organization receives T-shaped specialists who have a proactive position and are capable of self-organization by investing in the development of employees. The results of this study can be used by enterprises not only at the organizational level, but also through broadcasting in the education system to form an education ecosystem in accordance with the requirements of innovative development of a given region’s economy. Technologies of collective intelligence in the management of business processes of an organization https://bijournal.hse.ru/en/2022--2 Vol 16/670747110.html       With the digitalization of the economy, the creative component of an organization’s activities increases. Standard business process management methods stop working due to the rise in uncertainty of the task solution time. Currently, there are no effective technologies for managing intellectual activity processes in organizations. The role of collective intelligence technologies for knowledge management in organizations has long been discussed in the literature, but there are still no concrete proposals on implementation. This work aims to show how collective technologies can solve the problems of managing business processes of intellectual activity. The possibility of collective intelligence technologies for increasing labor productivity is demonstrated. Models for distributing tasks by competencies and synergy from collaboration are proposed for this demonstration. The paper shows that competencies are the primary metric that can be used to measure work with knowledge in an organization. But they should also be considered when organizing group activities. A simple model example shows that the correct distribution of tasks by competencies allows you to increase the speed of solving tasks by a group by several times. In real cases, calculations using computing resources are necessary. A model is also proposed that demonstrates increasing the joint activity of a creative employee and an analyst. It is shown that business process management should be supplemented by mapping the competence model and group work options to the stages of business processes. This will allow you to manage the business processes of intellectual activity. New energy efficiency metrics for the IT industry https://bijournal.hse.ru/en/2022--2 Vol 16/671664539.html       Reducing the technogenic impact of human activity on the ecology of the planet is a problem that is increasingly moving from a theoretical category into a practical one. The environmental situation is serious and requires more attention. One of the significant factors of the negative impact of humans on their environment is the emissions of harmful substances that occur during the production of electricity. The technical development of humanity and the widespread introduction of information technologies are characterized by an explosive growth in the number of electronic devices and the amount of data transmitted over information networks. This contributes to an increase in the need for computing resources for storing and processing this data, and as a result, the need for electricity is also increasing. Over the past 15–20 years, computing equipment has increased its computing power many times. The number of servers in operation is currently estimated at many millions of units, and the total energy consumption of the server park is becoming very significant in the structure of energy costs in all developed countries. In this article, we will analyze a way to reduce energy costs in the operation of servers and data centers, the application of which has a high potential for saving energy. We will give an example of a new way to evaluate the efficiency of IT equipment using a new factor – the server idle coefficient (SIC). Trusted artificial intelligence: Strengthening digital protection https://bijournal.hse.ru/en/2022--2 Vol 16/671667936.html       This article is devoted to aspects associated with the up-coming need for mass implementation of neural networks in the modern society. On the one hand, the latter will fully expand the capabilities of state institutions and society delegated to perform numerous tasks with higher efficiency. However, a significant threat to democratic institutions obliges society to set out the concept of reliable artificial intelligence (AI). The authors explore a new concept of a trusted AI necessary for the scientific and international community to counter improper future digital penetration. Explaining to what extent digital transformation is mandatory, the authors emphasize the numerous dangers associated with the applications of artificial intelligence. The purpose of the article is to study the potential hazards of neural networks’ abuse by the authorities and the resistance to them with reliance on the trusted AI. Studying various aspects of digital transformation and the use of artificial intelligence technologies, the authors formalize the dangers associated with the emergence and propose an approach to the use of digital protection technologies that can be trusted. On assigning service life for technical systems under inflation https://bijournal.hse.ru/en/2022--2 Vol 16/671669178.html       A technical system is used by an enterprise that is a typical market participant to perform specific work. During operation, the operating characteristics of the system deteriorate. In case of a possible failure of the system, it is decommissioned and this causes losses for the enterprise. It turns out that it is beneficial to assign a certain service life to the system after which (if no failure has occurred) it is subject to decommissioning. We are solving the problem of optimizing this assigned service life. Usually, when solving it, inflation is not taken into account, and the optimality criteria are the average costs per unit of time and other indicators that do not fully reflect the commercial interests of the enterprise owning the system. Using the principles and methods of valuation, we build a mathematical model and propose formulas that allow us, taking into account inflation, to find the optimal assigned service life of the system and at the same time estimate the market value of the work performed by the system and calculate the change in the market value of the system with age. Moreover, in this problem, the optimality criterion is the ratio of the expected discounted costs to the expected discounted volume of work performed by the system. We show that such a criterion maximizes the market value of the enterprise owning the system. We give examples of using the constructed model. The results obtained can be used both for solving other optimization problems of the reliability theory and for practical valuation of some types of machinery and equipment.