Sistema informático para la distribución de uniforme escolar. Caso de estudio: provincia de Granma, Cuba

The distribution of school uniforms in the province of Granma is a process that is carried out in the different study centers of the province. The current research paper is aimed at developing a computer system that allows the control of the distribution of school uniforms in the province of Granma. It proposes a new algorithm based on Q-Learning to optimize the scheduling of the process of making school uniforms in clothing workshops. The Q-Learning algorithm of Reinforced Learning is a solution to the problem of sequencing tasks with a Flow Shop environment in a real context. The development of the computer system is based on free and multiplatform technologies. The technologies are HTML 5, CSS 3, JavaScript, Bootstrap, jQuery and CodeIgniter. Extreme Programming was used as an agile methodology of development of software and the Model-View-Controller as an architectural pattern. A comparison of the results obtained from the execution of the algorithm with real data of the entity is performed. After analysis of the tests carried out, usefulness and reliability of the software developed are checked, which contributes to the improvement of the distribution of school uniforms in the province of Granma.

Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.