Multiparametric Optimization of Complex System Management Scenarios Based on Simulation Models

This work is devoted to the development of a multiparametric optimization module for a digital management decision support tool based on simulation models. It is noted that the optimization of simulation models of complex socioeconomic and sociotechnical systems involves the generation of multiple scenarios of system development, their calculation, and further comparison, which imposes additional requirements on the optimization algorithms used. Moreover, complex socioeconomic and sociotechnical systems are characterized by a multiplicity of goals, which leads to multiparametric optimization.  The result of the work is the algorithm for solving the problem of optimization of multiparametric scenario calculations using the example of a two-parameter optimization problem. The scope of the calculation optimization problem is to form the optimal set of scenarios that will ensure satisfactory computing time and, at the same time, give a representative scenario calculation result. Thus, the contribution of the current research is to formalize the processes of optimizing the parameters of simulation models of complex systems. In the course of the study, existing approaches to process optimization are considered. Based on the analysis of existing approaches to the formation of an optimal set of scenarios, ways to improve the algorithm type using approaches to scenario reduction or the introduction of genetic algorithms for the formation of an optimal set of scenarios are proposed. This work is carried out within a project to develop a digital tool to support managerial decision-making in sociotechnical and socioeconomic systems

Heuristic Approach to Planning Complex Multi-Stage Production Systems

The paper describes an algorithm for finding a quasi-optimal production plan for complex production systems that involve moving products through a chain of linked processes with varying resource sets. The general problem of optimizing a network of manufacturing enterprises is considered: a set of enterprises producing homogeneous products is modeled during a process consisting of a set of sequential operations arranged in a strict sequence. Standard methods for solving production planning problems are considered. According to the analytical review, most planning tasks in such systems are resolved using original techniques. For this reason, a universal heuristic algorithm was proposed. An algorithm with two branches is also proposed for two cases: for the case of a known constraint in the system and for an alternative case. The algorithm is focused on application in production systems in which the acquisition of empirical data is complicated by the large volume, heterogeneity, and limited reliability of data. In such systems, quasi-optimization in accordance with the proposed algorithm will allow for obtaining a satisfactory result with the permissible and required computing power. The algorithm can be classified as a greedy algorithm. It is partly based on local optimization and performs well for production with a long cycle and a small number of products. For this reason, the approach is recommended for heavy industry, shipbuilding, aircraft manufacturing, and other productions with a long cycle

Digital Platform for Modeling the Development of Regional Innovation Systems of Russian Federation

The paper aims at the design of a digital tool for analyzing the impact of scientific and technological progress on socioeconomic problems and sustainable development of the region. The research focuses on the consistent development of a digital platform for analyzing and visualizing digital data on regional innovation development, as well as predicting the sustainable development of regions based on the available regional infrastructure of innovation systems and the Russian regions' cluster structure. When designing the digital platform, we gave special attention to ensuring efficient data collection, processing, and analysis processes required for studying the socio-economic system. In the course of the work, an automated process of working with data was developed. The digital platform is being developed as a flexible tool for a wide range of users, from research centers, investors, and private enterprises to individual users interested in regional innovation development models. As part of the work, the process of selecting technical tools for the software implementation of the platform in terms of tasks and technical features of designing digital platforms is presented. The result of the work is a prototype of the Russian regional innovation system digital platform with the implemented functionality of a personal account, a module of simulation experiments, and various approaches to data analysis and visualization. The research is carried out as part of a project to develop a digital model of the regional innovation system of the Russian Federation as a driver of sustainable development

Intelligent Data Analysis for Infection Spread Prediction

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive

Intelligent Data Analysis for Infection Spread Prediction

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive

Complex Method of the Consumer Value Estimation on the Way to Risk-Free and Sustainable Production

Sustainable consumption and production strive for the rational management of natural resources, which implies a transition to the production of fewer goods with the greatest consumer value. Consequently, the consumer value assessment is a key task in the product and service design. However, a large number of applied practices for assessing consumer value is a challenge for researchers. Multiple heterogeneous solutions without a common classification and structure do not allow comparing methods with each other. Thus, there is a demand for some universal algorithm for assessing consumer value, which would be a model for the development of individual industry practices. Therefore, the present research aims to develop a universal algorithm for assessing consumer value, which is a unified sample. The work analyzes the current expertise in assessing consumer value. The paper provides a comparison of mathematical tools for aggregate indicators in order to develop a general formula for assessing consumer value. As a result, an algorithm for assessing consumer value has been developed, which includes the following stages: market segmentation by consumer groups, taking into account their personal characteristics and needs; product hierarchical division into groups according to indicators valuable to the consumer; selection of a scale for evaluating indicators; hierarchical convolution, calculation of the consumer value of selected indicators and their aggregation into a final assessment in accordance with coefficients obtained as a result of the initial data analysis. As part of the algorithm verification, an example of the implementation of the algorithm steps based on expert assessment of the tourist product characteristics is proposed. At the next stage of the study, a register of mathematical tools will be specified to ensure the implementation of the algorithm steps, and practical testing on real data on several products from different industries