Análisis y valoración de flexibilidad en el enrutamiento para implementación de un sistema de vehículos de guiado autónomo en un proceso de almacenamiento industrial simulado por computador

Se estudia el diseño y puesta en marcha mediante la simulación de un sistema AGV (vehículos de guiado automatizado por sus siglas en inglés), en un proceso de cargue para despachos en un almacén de mercancías, donde se evaluarán las principales variables a tener en cuenta de acuerdo con los parámetros y requerimientos de funcionamiento de la operación logística, revisando la literatura y metodologías recomendadas para la implementación de un sistema de AGV, se evaluó: el tipo de vehículo, espacio de almacenamiento, ruteo de vehículos, número de vehículos, posicionamiento del vehículo, programación de vehículos, administración de la batería y la demanda de cada producto en los despachos. Finalizado el diseño del sistema AGV se procedió a la recreación de las condiciones del almacén, carga para despacho y ruteo en un software de simulación para evaluar el desempeño y las dificultades de reorganizar el despacho de pedidos dentro del sistema, al comparar AGV con montacargas tradicionales, viendo una mayor eficiencia de operación para el primer caso.Abstract: The design and start-up is studied mean of simulation of an AGV system (automated guided vehicles), in a pickup process for shipments in a freight warehouse, where the main variables are taken into account will be evaluated according to the parameters and operating requirements of the logistics operation, reviewing the literature and recommended methodologies for the implementation of an AGV system, the following was studied: the type of vehicle, storage space, vehicle routing, number of vehicles, positioning of the vehicle, vehicle programming, battery management and the demand of each product in the offices. Once the design of the AGV system was completed the conditions of the warehouse, pickup for dispatch and routing in a simulation software were analyzed to evaluate the performance and the difficulties of reorganizing the order fulfillment within the system, when comparing AGV with traditional forklifts, checking a greater efficiency of operation for the first case.Maestrí

Modularity in artificial neural networks

Artificial neural networks are deep machine learning models that excel at complex artificial intelligence tasks by abstracting concepts through multiple layers of feature extraction. Modular neural networks are artificial neural networks that are composed of multiple subnetworks called modules. The study of modularity has a long history in the field of artificial neural networks and many of the actively studied models in the domain of artificial neural networks have modular aspects. In this work, we aim to formalize the study of modularity in artificial neural networks and outline how modularity can be used to enhance some neural network performance measures. We do an extensive review of the current practices of modularity in the literature. Based on that, we build a framework that captures the essential properties characterizing the modularization process. Using this modularization framework as an anchor, we investigate the use of modularity to solve three different problems in artificial neural networks: balancing latency and accuracy, reducing model complexity and increasing robustness to noise and adversarial attacks. Artificial neural networks are high-capacity models with high data and computational demands. This represents a serious problem for using these models in environments with limited computational resources. Using a differential architectural search technique, we guide the modularization of a fully-connected network into a modular multi-path network. By evaluating sampled architectures, we can establish a relation between latency and accuracy that can be used to meet a required soft balance between these conflicting measures. A related problem is reducing the complexity of neural network models while minimizing accuracy loss. CapsNet is a neural network architecture that builds on the ideas of convolutional neural networks. However, the original architecture is shallow and has wide layers that contribute significantly to its complexity. By replacing the early wide layers by parallel deep independent paths, we can significantly reduce the complexity of the model. Combining this modular architecture with max-pooling, DropCircuit regularization and a modified variant of the routing algorithm, we can achieve lower model latency with the same or better accuracy compared to the baseline. The last problem we address is the sensitivity of neural network models to random noise and to adversarial attacks, a highly disruptive form of engineered noise. Convolutional layers are the basis of state-of-the-art computer vision models and, much like other neural network layers, they suffer from sensitivity to noise and adversarial attacks. We introduce the weight map layer, a modular layer based on the convolutional layer, that can increase model robustness to noise and adversarial attacks. We conclude our work by a general discussion about the investigated relation between modularity and the addressed problems and potential future research directions

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words