Planejamento do plantio e da colheita de cana-de-açúcar utilizando técnicas matemáticas de otimização

• Ano de defesa: 2014
• Autor(a) principal: Ramos, Rômulo Pimentel [UNESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Cana-de-açúcar - Plantio; Cana-de-açúcar - Modelos matemáticos; Otimização matematica; Agricultura de precisão; Sugar-cane
• Link de acesso: http://hdl.handle.net/11449/110955

Due to the search for alternative sources of energy in recent years, sugarcane has come to stand out both domestically and in foreign markets, due mainly to ethanol and power cogeneration using sugarcane bagasse. To meet the resulting demand for sugarcane, high yields must be obtained in the biofuels industry, which requires proper planning of the sugarcane crop cycle from planting to harvest. One of the most important steps of this cycle is the planting, since well-planned planting results in a number of benefits, particularly increased production. Because these decisions affect the entire crop cycle, planning of planting is a complex task that requires great care. From this complexity comes the need for techniques that help corporate managers in the creation of a planting plan, and mathematical models can be used as just such a technique. In the present study, we formulate two optimization models to assist in planning sugarcane planting. The proposed methodology is divided into two parts. The first part divides the acreage into plots using a mathematical optimization technique of cuts in an effort to maximize sugarcane yield. The second part uses the proposed optimization model to choose the variety of sugarcane that should be planted in each plot and determine in which period of the year this planting should be done, thus maximizing total production over a five-year period. We also propose a genetic algorithm to solve this optimization model. We then present the results of computational simulations of plantings performed using these tools. The proposed methodology proves to be an effective tool for optimized planning the planting of sugarcane, producing a reduction in the number of maneuvers over 40% and increasing production in 17,8% in the fields considered.