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Està subjecte a una llicència de Reconeixement-NoComercial-SenseObraDerivada 4.

In this paper the obtaining of an optimum policy in the capacity expansion planning of a particular thermal‐electric power system is proposed. Therefore, a two‐stage stochastic integer programming is formulated. The model includes, through a finite group of scenarios, the existent uncertainty related to the future availability of the thermal plants currently under operation. The resultant model is solved numerically by the application of the L‐shaped method, whose implementation and development were executed using the software AMPL, with CPLEX as a solver. The results reached are shown, which validate the use of the methodology adopted in this work.

The process for agriculture planning starts by delineating the field into site-specific rectangular management zones to face within-field variability. We propose a bi-objective model that minimizes the number of these zones and maximizes their homogeneity with respect to a soil property. Then we use a method to assign the crops to the different plots to obtain the best profit at the end of the production cycle subject to water forecasts for the period, humidity sensors, and the chemical and physical properties of the zones within the plot. With this crop planning model we can identify the best management zones of the previous bi-objective model. Finally, we show a real-time irrigation method to decide the amount of water for each plot, at each irrigation turn, in order to maximize the total final yield. This is a critical decision in countries where water shortages are frequent. In this study we integrate these stages in a hierarchical process for the agriculture planning and empirically prove its efficiency.

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