An electrocardiogram (ECG) is a noninvasive study used for the diagnosis of cardiac arrhythmias (CAs). The identification of a cardiac arrhythmia depends on its classification. This classification has been approached through different strategies, both mathematical and computational. In this work, a new computational model based on the particle swarm optimization (PSO) algorithm and convolutional neural network (CNN) is proposed for the classification of five classes of CAs obtained from the MIT-BIH Arrhythmia Dataset (MITDB). The goal of the PSO is to optimize the hyperparameters that define the layered architecture of a CNN, to increase the accuracy and decrease the categorical cross-entropy (CE) error. The proposed model found a satisfactory layered architecture in 17.68 hours, obtaining an Accuracy of 98% and 97%, a CE of 0.044968 and 0.084768, in training and testing, respectively. These results demonstrate that the proposed model is reliable and also represents an innovative approach because it allows dispensing with the manual selection of the hyperparameters of the layered architecture of a CNN.

We study the optimal production planning and major maintenance scheduling for an unreliable manufacturing system. We assume that the production unit experiences progressive deterioration that negatively influences product quality. For the production policy, we extend improve traditional threshold policies with a superior alternative, based on a just-in-time (JIT) strategy. The paper brings a new vision on the importance of implementing more effective production strategies based on JIT methods, instead of traditional threshold policies. When a failure occurs, the production unit is minimally repaired, and when the major maintenance is selected, the machine is restored to brand-new conditions. The objective of the model is to determine the simultaneous JIT production and major maintenance strategy that minimizes the total cost. Due to the stochastic features of the system, a simulation-based optimization approach is proposed, which combines the descriptive capabilities of simulation modeling with analytical models, statistical analysis, and optimization techniques. The results verify that the proposed simulation–optimization approach provides new and coherent results that highlight the strong influence of quality deterioration on the determination of the control parameters. A sensitivity analysis and a comparative study are conducted to illustrate that significant cost savings could be obtained with the proposed approach.

This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Most procedures that deal with this problem reside on linear programming methods, heuristics, metaheuristics, and hybridizations. These methods face drawbacks like handling only low-complexity instances or requiring extensive parameter tuning. To address these limitations we develop a Petri-net model to construct cutting patterns. Using the filtered beam search algorithm, the reachability tree of the Petri net is constructed level by level from its root node to find the best solution, pruning the nodes that worsen the solution as the search progresses through the tree. Our algorithm is compared with the Least Lost Algorithm and the Generate and Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal and near-optimalsolutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem.

En el presente artículo de investigación se diseñó una herramienta cuantitativa la cual consistió en un checklist y una evaluación visual para conocer el % de cumplimiento de las 5´S en el departamento de peletizado de una empresa dedicada al reciclado y fabricación de bolsas plásticas debido a que se presento un decremento en la calidad y productividad del proceso de peletizado después de reiniciar operaciones en 2024 derivado de una interrupción causada por el virus SARS CoV2. La herramienta se enfocó en 25 puntos de revisión haciendo especial énfasis en la clasificación, orden y limpieza. Esta permitió evaluar el grado de cumplimiento de cada una de las 5S permitiendo detectar los principales problemas y así proponer acciones de mejora, sus respectivos responsables y sus beneficios para incrementar la calidad y productividad. Una vez implementadas las acciones se evaluó nuevamente el grado de cumplimiento de las 5´S logrando un incremento considerable en el % de cumplimiento.

Cutting and packing problems have been studied for decades and are of concern for a wide variety of industries to reduce costs and material utilization. This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Procedures that address this problem include linear programming methods and metaheuristics. However, linear programming methods are limited to low-complexity instances, while most metaheuristics require extensive parameter tuning. In this paper, we develop a Petri-net model to construct cutting patterns. Using the reachability tree of the Petri net, the filtered beam search algorithm is implemented to find the best solution. Our algorithm is compared with the Least Lost Algorithm and the Generate & Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal solutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem.