In this work we build the stochastic adding machine in base 2 considering the truncation matrix Sn associated to the operator S and we study the eigenvalues of the matrix Sn acting in l∞(N).

In this work we build the stochastic adding machine in base 2 considering the truncation matrix Sn associated to the operator S and we study the eigenvalues of the matrix Sn acting in l∞(N).

In the modeling of many problems on linear optimization is not possible to consider the classic deterministic model because the set of parameters is not fully known due to the significant variation of the data along time or because there is no uniformity on the values. These kind of problems are known as problems with uncertainty and there are different approaches about modeling and methods of solution to resolve them. In this paper we make a review of such approaches focusing basically in stochastic optimization, fuzzy optimization, intervaling optimization and hybrid optimization. The difference between these approaches is perceived in the nature of the data, notions of feasibility and optimality and computational requirements, among others.

In real world, uncertainty is a important factor to take into account otherwise the schedules face the risk of failing, and/or to become unviable. Therefore, when creating schedules we must consider theuncertainty to prevent or repair the undesired effects. In this work we make a review of scheduling with uncertainty to respond the following questions: How can we represent the uncertainty? and What methodsdo exist to treat uncertainty? Likewise, we show the different ways in how the uncertainty is present in areas like the petrochemistry, the production, the transport, the energy, etc. We also make a study about two questions that all scheduling must respond: When to programme? and how to programme?

In the modeling of many problems on linear optimization is not possible to consider the classic deterministic model because the set of parameters is not fully known due to the significant variation of the data along time or because there is no uniformity on the values. These kind of problems are known as problems with uncertainty and there are different approaches about modeling and methods of solution to resolve them. In this paper we make a review of such approaches focusing basically in stochastic optimization, fuzzy optimization, intervaling optimization and hybrid optimization. The difference between these approaches is perceived in the nature of the data, notions of feasibility and optimality and computational requirements, among others.

In real world, uncertainty is a important factor to take into account otherwise the schedules face the risk of failing, and/or to become unviable. Therefore, when creating schedules we must consider theuncertainty to prevent or repair the undesired effects. In this work we make a review of scheduling with uncertainty to respond the following questions: How can we represent the uncertainty? and What methodsdo exist to treat uncertainty? Likewise, we show the different ways in how the uncertainty is present in areas like the petrochemistry, the production, the transport, the energy, etc. We also make a study about two questions that all scheduling must respond: When to programme? and how to programme?

In this work, a basic epidemiological model is used to determine the evolution of COVID-19 in each of the regions of Peru. For determining the parameters of the model which characterize a certain epidemic, the reports of infected, deceased and recovered people provided by the Regional Health Management of Peru are used. As a result, we obtained the configuration of the infected, susceptible and removed which are consistent with the existing bibliography, thus we also obtain a time interval in which there is a considerable number of infected, the maximum number of infected and the date on which it occurs.

In this work, a basic epidemiological model is used to determine the evolution of COVID-19 in each of the regions of Peru. For determining the parameters of the model which characterize a certain epidemic, the reports of infected, deceased and recovered people provided by the Regional Health Management of Peru are used. As a result, we obtained the configuration of the infected, susceptible and removed which are consistent with the existing bibliography, thus we also obtain a time interval in which there is a considerable number of infected, the maximum number of infected and the date on which it occurs.