Optimization models under uncertainty and Artificial Intelligence algorithms to the Planning and Coordinate of the Processing Tomato Agroindustry

• Grant number: 23/04285-1
• Support Opportunities: Regular Research Grants
• Start date: November 01, 2023
• End date: October 31, 2025
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Cleber Damião Rocco
• Grantee: Cleber Damião Rocco
• Host Institution: Faculdade de Ciências Aplicadas (FCA). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil

Abstract

This research project aims to develop an Intelligent Support System for decision making to improve the planning and coordination of tactical and strategic activities in the tomato processing agroindustry through the integration of analytical approaches from Operational Research (OR) and Artificial Intelligence (AI) algorithms. It is intended to use mathematical programming and robust optimization to model agricultural (planting, harvesting, transport etc.) and industrial operations (process selection, production definition, inventories etc.) and use machine learning algorithms to perform yield crop and industrial yield parameters, since they are currently very difficult to be assumed due mainly to wheather conditions, in addition to this agro-industrial system demonstrating inaccuracies in logistical decisions, such as in defining the demand for final products from consumers and tomato pulp concentrated for smaller agroindustries, in addition to the frequent indetermination of the availability of planting area due to competition with other crops, such as corn etc. These two analytical approaches (OR and AI) will be coupled to make a single system that will be able to outline all the tactical and strategic planning decisions of companies in this sector, also considering aspects of environmental sustainability (water and CO2 emission/sequestration). Tools developed in the research will be able to contribute both in the real practice of the companies and in the scientific knowledge of the area of modern agroindustrial management. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DONIZETTE, ANDRE CINTAS; ROCCO, CLEBER DAMIAO; DE QUEIROZ, THIAGO ALVES. Predicting leishmaniasis outbreaks in Brazil using machine learning models based on disease surveillance and meteorological data. OPERATIONS RESEARCH FOR HEALTH CARE, v. 44, p. 16-pg., 2024-11-27. (23/04285-1)