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The Operation Planning of Hydroelectric Systems is a large, time-coupled, stochastic, space-coupled and nonlinear optimization problem. The formulation of such problem can have several conflicting objectives in the representation of different aspects of the problem. In this work, we propose two approaches for the study and resolution of this problem. The proposals are based on two Evolutionary Metaheuristics-Genetic Algorithms and Differential Evolution. The methods work simultaneously with a set of solutions in order to perform exploration and exploitation of the search space. The intent is to find a set of solutions obtained in a single round of the algorithm, considering explicitly the different criteria of the problem. The proposed algorithms are applied to Brazilian hydropower case studies and successfully generated a wide range of cost-benefit solutions that can be applied in real planning.

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Technology and innovations have long improved farming over the world and, as Industry 4.0 quickly spread, farmers have embraced high-level automation and data exchange, driving a transformation called Farming 4.0. Consequently, precise and even real-time field information have become easily accessible. Though, analyzing all this information requires great skills and tools, like mathematical knowledge and powerful computational algorithms to reach farmers expectations. This research explores the Crop Rotation Problem (CRP) and its relevance for the integration of Precision Agriculture (PA) and farm management. This paper presents a new mathematical approach for the CRP based on the nutrient balance and crop requirements, increasing the sustainable appealing of the problem. A real-encoded genetic algorithm (GA) was developed for optimization of the CRP. The results indicate good performance in mid and long-term crop scheduling.