Nonlinear Input-Output Balance and Young Duality: Analysis of Covid-19 Macroeconomic Impact on Kazakhstan

We discuss the possibilities of the new approach to the interindustry linkages modeling for the analysis of regional macroeconomic effects of Covid-19. Our approach is based on the mathematical framework of nonlinear input-output balance that allows to find the equilibrium point in the set of industry inputs and prices by solving the primal nonlinear resolute allocation problem and the Young dual problem of prices formation. We identify and calibrate the model on the base of aggregated official input-output statistics of Kazakhstan. Given the scenario conditions for primal factors prices and final consumption in the economy the model allows to evaluate the new competitive equilibrium in the production network. The advantage of the model is non-linearity of balances and technologies that allows substitution of industry inputs. In the case of technologies with constant elasticity of substitution (CES) we apply the model to analysis of macroeconomic responses of the Kazakhstan economy to the Covid-19 pandemic.

A Generic Approach for Allocating Movement Permits During/Outside Curfew Period during COVID-19

During the coronavirus disease (COVID-19) pandemic, different exciting concepts around solutions, technical components, smartphone applications, and novel wireless services are needed to adapt to the new lifestyle standard that emerged from the COVID-19 crisis. In this context, social distancing was imposed to prevent or decrease significantly further transmission of the COVID-19. In other words, research results have shown that slowing the spread of COVID-19 is the way to save people's lives and relieve the burden on health-care systems. This social distancing can be tracked using cell phone movement/data. This paper presents a new approach/algorithm for allocating and optimizing/adapting the movement permits during/outside curfew periods inside workplaces, buildings, companies and institutions. This approach is an effective tool to reduce the spread of COVID-19 by promoting health safety during the pandemic, especially in places where social distancing can be difficult. Consequently, this paper presents a technological solution to automate the process of giving movement permits in workplaces. The paper results showed that the proposed strategy of social distancing inside buildings is effective enough to flatten the curve. Furthermore, health authorities are not required to recommend staying home to slow the spread of COVID-19. Consequently, this paper introduces a solution for the resource sharing problem (resource allocation problem), where multiple agents (people or robots) of a system trying to move reliably in their environment. The biggest concern of these agents is to avoid collisions (infections). As a result, the experiments carried out in this paper showed the high performance of the designed algorithm complying with COVID-19 social distancing regulations.

Resource Allocation Strategy during COVID-19 Period and Linear Programming Model Based on Three Meta-Heuristic Algorithms

English Teachers resource allocation problem (TRAP) which is a highly complex multi-level system is a talent scheduling problem (TSP) with limited human, material and financial resources. It is of great significance to study the allocation of teacher resource in a century-long plan based on education. In this paper, under the effective control of COVID-19, taking the Bayannur City of Inner Mongolia as an example, teaching sites are set up to study the TRAP for the resumption of classes in the graduating grade. In order to minimize the total cost of the whole distribution system, a multi-objective linear hybrid model (MOLHM) is proposed based on the fact about different demands on the number of teachers in each site, the different daily salary of teachers with different teaching experience and degree level, and the different cost of transporting teachers to respective destination. And three heuristic algorithms, ant colony optimization algorithm (ACOA), tabu search algorithm (TSA) and particle swarm optimization algorithm (PSOA) are used to solve the model. Through numerical experiments, the feasibility of them is verified, and the performances of them is compared in terms of optimization results and running time. In the system of the paper, the optimization result of ACOA is optimal, and TSA has better performance of running time. Under the condition that the equal number of ants and particles, the running time of PSOA is better than that of ACOA. © 2021 ACM.