In this paper, a model is proposed based on the different levels of social restrictions for the COVID-19 spread restraint in Iran. Also, a Genetic Algorithm (GA) identifies parameters of model using reported main data from the Iranian Ministry of Health and simulated data based on proposed model. Whereas Model Predictive Control (MPC) is a popular method which has been widely used in process control, after the discretization of model by a common method like Euler method, then we can consider the appropriate constraints and solve online optimization problem. In this paper, we have shown that the MPC controller able to flatten infected (symptomatic) individual curve and decrease its peak by applying the different levels of social restrictions. Numerical example and simulation results, based on main data, are given to illustrate the capability of this method.

In this paper, we study an optimal control problem in which their cost function is interval-valued. Also, a stochastic differential equation governs their state space. Moreover, we introduce a generalized version of Bellman's optimality principle for the stochastic system with an interval-valued cost function. Also, we obtain the Hamilton-Jacobi-Bellman equations and their control decisions. Two numerical examples happen in finance in which their cost function are interval-valued functions, illustrating the efficiency of the discussed results. The obtained results provide significantly reliable decisions compared to the case where the conventional cost function is applied.