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El objetivo de este artículo es desarrollar un algoritmo genético el cual permita determinar los tamaños de lote de producción y su programación en un sistema de manufactura de una máquina para órdenes multiproducto, cuya función objetivo minimiza la suma de los costos de inventario por terminaciones tardías y de alistamiento. El problema contempla un conjunto de órdenes a ser procesadas con sus respectivas fechas de entrega. Cada orden debe ser entregada en su totalidad. Dentro de la programación de los trabajos se consideran tiempos de alistamiento dependientes de la secuencia. En la metaheurística implementada se utiliza de manera embebida un método heurístico para el cálculo de la función de adaptación. El método heurístico presentado es una variación del Optimal Timming Algorithm el cual involucra los tiempos de alistamiento dependientes de la secuencia. Se desarrolla un diseño de experimentos para probar el desempeño del algoritmo utilizando instancias generadas de forma aleatoria y comparando sus soluciones contra las encontradas por un método exacto. Los resultados muestran que el algoritmo logra un buen desempeño tanto en tiempo de ejecución como en calidad de la solución especialmente en instancias grandes. The main purpose of this paper is to develop a hybrid genetic algorithm in order to determine the lot sizes and their production scheduling in a single machine manufacturing system for multi-item orders, the objective function minimizes the sum of holding costs, tardy costs and setup costs. The problem considers a set of orders to be processed each one with its own due date. Each order must be delivered complete. In the scheduling are considered sequence dependent setup times. The proposed hybrid genetic algorithm has embedded a heuristic that is used to calculate its fitness function. The heuristic method presents a modification on the optimal timming algorithm in which are involved sequence dependent set up times. A design of experiments is developed in order to assess the algorithm performance, which is also tested using random-generated data and results are compared with those generated by an exact method. The results show that the algorithm achieves a good performance in both solution quality and time especially for large instances.

This paper proposes an alternative linear control technique to maximize the energy capture in a horizontal-axis wind turbine. The proposed strategy is based on Generalized Proportional Integral (GPI) controllers supported by the active disturbance rejection approach, which allows asymptotic tracking of a rotor speed reference trajectory without exact wind turbine model knowledge. The proposed methodology controls the tip-speed ratio, via the rotor angular speed, to an optimum point at which power coefficient is maximum. Several simulations are performed on a 4.8MW wind turbine benchmark model in order to validate the proposed control strategy and to compare it to a classical controller. The simulation results show that the proposed control strategies are effective in terms of power capture and robustness.