RESUMO
This article presents a simulation model of the effect of the COVID-19 Biosafety protocol in the Queues for the return to face-to-face classes as a basis for the return of educational activities. The restrictive measures to face the COVID-19 pandemic, greatly affected the normality of the development of activities carried out in higher education, constituting a great challenge since the measures and conditions of return and access to institutions are different from what was previously carried out before the pandemic. The objective of this article is to carry out a simulation of queues through SIMIO (Logistics process simulation software), where the number of students expected per day and the average entry times are taken into account to adjust them to an exponential distribution, from this information loaded into the simulation it is They will determine if the times increase or decrease taking into account the theory of queues and conditions presented in the study such as the probability distribution of the times of each of the processes involved in the process of accessing the University facilities.
RESUMO
The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics. Automatically revising the space constituted by combining these techniques to find the most adequate combination has received less attention. In this paper, we propose exploring the heuristics space for the vertex coloring problem using evolutionary algorithms. We automatically generate three new algorithms by combining elementary heuristics. To evaluate the new algorithms, a computational experiment was performed that allowed comparing them numerically with existing heuristics. The obtained algorithms present an average 29.97% relative error, while four other heuristics selected from the literature present a 59.73% error, considering 29 of the more difficult instances in the DIMACS benchmark.