This paper explores transportation cost minimization in the vehicle routing problem under carriers’ cooperation on large-scale transport networks. The underlying model is a dynamic cooperative game...

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%.

Power grid architectures need to be revised in order to manage the increasing number of producers and, more generally, the decentralisation of energy production and distribution. In this work, we describe a multi-supplier multi-consumer congestion model of a power grid, where the costs of consumers depend on the congestion in nodes and arcs of the power supply network. The consumer goal is both to meet their energy demand and to minimise the costs. We show that the methods of non-atomic routing can be applied in this model in order to describe current distribution in the network. We formulate a consumer cost minimisation game for this setting, and discuss the challenges arising in equilibrium search for this game.

This paper studies a game-theoretic model of traffic flow assignment with multiple customer groups and the BPR delay function on a parallel channel network. We prove the existence of a unique Nash...

The consideration of infectious diseases from a mathematical point of view can reveal possible options for epidemic control and fighting the spread of infection. However, predicting and modeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemic of a new type of virus based on the example of COVID-19. The important result of the study is the description of the principle of dynamic balance of epidemiological processes, which has not been previously used by other researchers for epidemic modeling. The new approach is also based on solving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered and dead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the future trajectories of stochastic parameters, which are called the percentage increase in the total number of confirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the considered indicators of the COVID-19 epidemic, in Russia and European countries, during the first wave of the epidemic.