Search

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]

• New optimization model for integrated planning of sugarcane cultivation. • Integrated cultivation of sugarcane and energy-cane for energy, sugar and ethanol. • Developed a specific heuristic to solve the mathematical model for real data. • Comparison between exact and heuristic solutions. The main products obtained from sugarcane for the global market are sugar, ethanol and energy. Although the demand for these products is high, in Brazil there is no longer space to expand sugarcane cultivation due to other important food crops and environmental protection areas. Therefore, current research seeks ways to increase sugarcane productivity without expanding the planted area. Consequently, a type of cane with higher concentration of fiber has been developed and cultivated called energy-cane. This cane type has more fiber than sucrose in its composition, which makes it better for producing energy and ethanol and inappropriate for sugar production. To meet the demands for these three products, both energy-cane and sugarcane are needed, making the planning and decision-making more difficult in the energy-sugar sector. In this context, a mixed integer linear optimization model for the integrated planning of the planting and harvesting of sugarcane and energy-cane for a sugar-energy plant is proposed to determine an optimized planting and harvesting schedule, that aims to maximize the sucrose and fiber production in the cane fields. The model takes into account a flexible harvest period to meet the demands of the consumer market and respect the operational constraints of the company, which can cause variations in productivity. To solve real-life instances where exact methods fail, a heuristic based on the relax-and-fix and fix-and-optimize concepts is proposed. Numerical experiences with random instances were generated to test both the model and the heuristic. Feasible solutions with an error close to 1% were obtained, consuming one tenth of the CPU time, on average. Thus, the mathematical model and the heuristic method developed have great potential to support decision-making in sugarcane and energy-cane production planning. [ABSTRACT FROM AUTHOR]