Dissertation defense (December 19, 2024): Roberto da Silva Gervasio Pontes
Student: Roberto da Silva Gervasio Pontes
Title: Multiobjective Optimization for Crop Rotation Planning Problems
Advisores: Laura Assis (advisor) e Diego Brandão (co-advisor)
Committee: Laura Assis (Cefet/RJ), Diego Brandão (Cefet/RJ), Felipe Henriques (Cefet/RJ), Fábio Usberti (UNICAMP)
Day/Time: December 19, 2024 / 9:30 a.m.
Abstract: Agriculture is considered an essential pillar of the world economy and is at the center of contemporary societies. In recent decades, the sector has consolidated and radically changed the man-environment relationship to face the food supply crisis and ensure food security. However, there is still great concern about food security, given the expected population growth of more than 9.5 billion in 2050. In this context, the introduction of computerization in agrifood supply chains has been increasingly significant. Modern agriculture is becoming increasingly interdisciplinary, seeking a potential increase in the productivity of this sector and the promotion of more sustainable practices. Precision agriculture is one of these practices that have one of the important tools for CPP and the CRPP}that greatly impact the environment and productivity. Optimizing these issues can enable the industry to respond to climate change, provide healthy and safe food, and produce cost-effective food. In this way, the present work presents models using Linear Programming for MCPP and MCRPP to maximize the net income, maximize the diversity of cultures, and maximize the area used. The results were compared to models with the same data set found in the literature, the only objective of which was to maximize net income. It was found that the proposed models show an average increase of on average, 60% in the diversity of crops planted with net return losses of less than 5%. The evaluation of scenarios using the Weighted Sum and ε-constraint methodologies effectively explored the Pareto optimal frontier. Although the ε-constraint method involves higher computational costs, it stands out for its superior ability to discriminate the trade-offs between objectives.
