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Sugarcane is one of the main commodities in the world economy. An important step in the sugarcane supply chain is the harvest stage, requiring the support of mathematical modeling and optimization tools. Integrating the process of choosing the areas for harvesting according to the sugarcane maturation with the formation of the harvesting fronts and associating them with machine relocation costs is challenging. This paper contributes with a novel integer mathematical model that considers these concerns using three conflicting objectives. The first is that the sugarcane should be harvested as close as possible to its maturity, considering the sugarcane should be harvested as close as possible to its maturity by considering a combined measure of temperature and time since planting. The second aims to minimize the number of harvest fronts. The third objective is to minimize the costs involved with the relocation of the harvesters that cut the cane. Different strategies for obtaining Pareto-optimal solutions were proposed using exact methods, including MIP start and a decomposition technique. In the worst case, solutions with tolerable integrality gaps were obtained (inferior to 3%) in less than 1.5 h. Computational tests in real case studies were conducted in the sugarcane industry in two countries, indicating strong trade-offs in the determined solutions. In the Brazilian case, harvesting as close to maturation as possible, for example, causes an increase of 158% in the number of harvest fronts and 47% in the transportation costs, considering the minimum values for these objectives. Other trade-offs were seen and discussed when one of the other objectives was prioritized. These different compromise solutions help the decision-makers choose the most appropriate harvesting schedule according to their preferences. • We propose a new MIP optimization model to determine the harvesting of sugarcane. • The model aims to optimize three conflicting objectives. • A new method of computing cane maturation is used: the concept of degree-days. • A procedure for determining Pareto solutions by exact methods is proposed. [ABSTRACT FROM AUTHOR]

Abstract: Time series of optical satellite images acquired at high spatial resolution is a potentially useful source of information for monitoring agricultural practices. However, the information extracted from this source is often hampered by missing acquisitions or uncertain radiometric values. This paper presents a novel approach that addresses this issue by combining time series of satellite images with information from crop growth modeling and expert knowledge. In a fuzzy framework, a decision support system that combines multi-source information was designed to automatically detect the sugarcane harvest at field scale. The formalism that we used deals with the imprecision of the data and the approximation of expert reasoning. System performances were analyzed using a time series of SPOT-5 images. Results obtained were in substantial agreement with ground truth data: overall accuracy reached 97.80% with stability values exceeding 89.21% for all decisions. The contribution of fuzzy sets to overall accuracy reached 15.08%. The approach outlined in this paper is very promising and could be very useful for other agricultural applications. [Copyright &y& Elsevier]

In this paper a new revised multi-choice goal programming (RMCGP-LHS) model is proposed to deal with uncertainty in sugar cane harvest scheduling for sugar and ethanol milling companies. The RMCGP-LHS model uses a weekly decision-making horizon and takes into account the time and condition of land management, cane cutting decisions, and agricultural logistics. Its objective is to obtain information in order to harvest sugar cane plots in the period closest to the highest saccharose levels, while also minimizing agro-industrial costs. The RMCGP-LHS model was applied to a real case sugar and ethanol mill, and its optimization has provided harvesting policies that were validated by the company’s managers. Besides that the RMCGP-LHS model is a very practical tool for simulating in a fast way different scenarios involving uncertainties on model parameters and helping the managers in decision making process in real time. [ABSTRACT FROM AUTHOR]