A genetic algorithm for crop rotation

• Autor(a) principal: Filho, Angelo Aliano [UNESP]
• Data de Publicação: 2012
• Outros Autores: De Oliveira Florentino, Helenice [UNESP], Pato, Margarida Vaz
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/73380
• topic: Crop rotation; Genetic algorithm; Optimization; Complex combinatorial problem; Computational experiment; Computational time; Constructive heuristic; Crop sequencing; Feasible solution; Initial population; Planted areas; Crops; Mathematical models; Profitability; Genetic algorithms

In the last few years, crop rotation has gained attention due to its economic, environmental and social importance which explains why it can be highly beneficial for farmers. This paper presents a mathematical model for the Crop Rotation Problem (CRP) that was adapted from literature for this highly complex combinatorial problem. The CRP is devised to find a vegetable planting program that takes into account green fertilization restrictions, the set-aside period, planting restrictions for neighboring lots and for crop sequencing, demand constraints, while, at the same time, maximizing the profitability of the planted area. The main aim of this study is to develop a genetic algorithm and test it in a real context. The genetic algorithm involves a constructive heuristic to build the initial population and the operators of crossover, mutation, migration and elitism. The computational experiment was performed for a medium dimension real planting area with 16 lots, considering 29 crops of 10 different botanical families and a two-year planting rotation. Results showed that the algorithm determined feasible solutions in a reasonable computational time, thus proving its efficacy for dealing with this practical application.