Una nueva estrategia heurística para el problema de Bin Packing

ResumenEl problema de Bin Packing (BPP) es NP-duro, por lo que un método exacto para resolver instancias del BPP requiere un gran número de variables y demasiado tiempo de ejecución. En este trabajo se propone una nueva estrategia heurística para resolver instancias del BPP en donde se garantiza la solución óptima. La estrategia propuesta incluye el uso de un nuevo modelo exacto basado en arcos de flujo. En el modelo propuesto, el número de variables se redujo asignando objetos en contenedores. Adicionalmente se incluye una heurística que mediante el preprocesado de la instancia permite reducir su tamaño y con ello el espacio de búsqueda del algoritmo de solución. Para validar el enfoque propuesto, se realizaron experimentos usando los conjuntos de prueba hard28, 53nirup, bin1data, uniform, triplets y subconjuntos de otras instancias, todos ellos conocidos en el estado del arte. Los resultados muestran que empleando nuestro enfoque es posible encontrar la solución óptima de todas las instancias de prueba. Además, el tiempo de ejecución se redujo en relación con lo reportado por el modelo basado en arcos de flujo. Las reducciones de tiempo fueron de 19.7 y 43% para los conjuntos 53nirup y hard28, respectivamente.AbstractThe Bin Packing problem (BPP) is NP-hard, the use of exact methods for solving BPP instances require a high number of variables and therefore a high computational cost. In this paper a new heuristic strategy for solving the BPP instances, which guarantees obtain optimal solutions, is proposed. The proposed strategy includes the use of a new model based on flow arcs. In the proposed model, the number of variables was reduced by previous allocation of objects in bins. Additionally, our approach includes a heuristic that allows reducing the instance size and thereby the solution algorithm search space. To validate the proposed approach, experiments were performed using the test sets hard28, 53nirup, bin1data and falkenauer, all of them well known in the state of the art. The results show that it using our approach is possible to find the optimal solution for all test set. Also, the execution time was reduced in regard the reported time obtained by using the flow arc model. Time reductions were up to 19.7 and 43% for 53nirup and hard28 test set, respectively

A proximal chemical analysis in craft beer solid waste, and its acceptance in sows

ABSTRACT A proximal chemical analysis on organic solid waste was performed from three types of craft beer and its acceptance in the feeding of sows was detected. To determine the humidity, a stove was used at 105 °C for 24 hours, the ethereal extract was determined using a Soxhlet equipment and ethyl ether as solvent, the determination of ash content was made by calcination in a muffle at 700 °C; for crude fiber analysis, acid digestion with 0.2 N sulfuric was used, the crude protein was determined using a Kjedahl equipment in order to analyze the total nitrogen, the nitrogen-free extract, was determined by the difference of 100 % minus the addition of moisture, ash, fat, protein, and fiber. The total of digestible nutrients was computed by adding the digestibility of all organic compounds, and acceptance of the food was made through offering it as a first option to the sows from two farms. The organic solid residues of craft beer contain an average 2.43% of ash, 1.99% of ethereal extract, 4.91% of crude fiber, 64.20% of nitrogen-free extract, 10.91% of crude protein, and 73.47% of total digestible nutrients. The food achieved an acceptance of 83.4% when it was offered alone and 100% combined with other ingredients.RESUMEN Se realizó un análisis químico proximal en residuos sólidos orgánicos de tres tipos de cerveza artesanal y detecto su aceptación en la alimentación de cerdas. Para determinar humedad de residuos de cerveza se utilizó estufa a 105°C durante 24 horas, el extracto etéreo se determinó con equipo Soxhlet y éter etílico como solvente, la determinación de cenizas se realizó por calcinación en mufla a 700°C, para fibra cruda se utilizó digestión ácida con ácido sulfúrico 0.2 N, para proteína cruda se empleó equipo Kjedahl, el extracto libre de nitrógeno se determinó por diferencia del 100% de la suma de humedad, cenizas, grasa, proteína y fibra. El total de nutrientes digeribles se realizó mediante la sumatoria de la digestibilidad de los compuestos orgánicos. Los residuos sólidos orgánicos de cerveza artesanal contienen en promedio 2.43% de cenizas, 1.99% de extracto etéreo, 4.91% de fibra cruda, 64.20% de extracto libre de nitrógeno, 10.91% de proteína cruda y 73.47% de nutrientes digestibles totales. La determinación de la aceptación del alimento se realizó ofreciéndolo como primera opción a las cerdas de dos granjas. El alimento tuvo una aceptación de 83.4% cuando se ofreció solo y del 100% combinado con otros ingredientes

FUZZY LOOK-UP TABLE FOR KNOWLEDGE MANAGEMENT AND DECISION MAKING

The knowledge management is one of the most important tasks in education. The main problem presented in this process is the transmission of the knowledge and the understanding of the student. In this area, the interpretation produces variations of a concept and this condition produces errors. The use of the expert systems provides an interpretation with a degree of freedom to evaluate a concept and allows the knowledge management without expertise. The results obtained show that the expert approach provides an evaluation with a tolerable variation accepted by the principal organizations dedicated to measurement and standardization

Visual analysis of differential evolution algorithms

In this article a web tool which contributes to the visual analysis of the Differential Evolution (DE) algorithms is presented. The tool provides a graphic interface with 8 views that allows understanding the underlying process of the algorithm. The tool has a library which extracts data from DE algorithms and its main feature is that the functions of the library can be embedded in the code of any DE algorithm to be analyzed. To validate the tool, three DE algorithms: DE/Rand/1/bin, DE/best/1/bin, and JADE and three test functions: Sphere, Rosenbrock, and Rastrigin have been used, which produced a total of 234 different tests, all of them performed successfully. The tool can allow to experts to analyze algorithms, particularly DE algorithms, and it can contribute to improve such algorithms or in generating new strategies that can emerge from the analysis of the extracted information