Optimization of Production through Lot-Sizing and Scheduling on Parallel Production Lines
Principal investigators: Sergio Cavero, Eduardo G. Pardo
Type: R&D Contract Project
Funding entity: Confidential (not disclosed)
Period: February 2024 – December 2024
Description
This R&D contract project addresses the optimization of industrial production processes, specifically the joint problem of lot-sizing and scheduling on parallel production lines. Models and algorithms are designed to minimize production costs and times while satisfying capacity constraints and delivery deadlines.
For confidentiality reasons, no further information is provided about the contracting entity or the specific details of the project.
Sergio Cavero
Phd in Artificial Intelligence
Sergio Cavero was born Madrid (Spain) on September 24, 1997. He graduated in Software Engineering from Universidad Politécnica de Madrid in 2019. During his undergraduate studies he made a stay at the University of Bradford (UK). In addition, he was awarded twice with the ‘Beca de Excelencia of the Comunidad de Madrid, and also awarded for the Best Final Degree Project. Later, he completed a Master’s Degree in Artificial Intelligence at the same university (UPM) obtaining awards for Best Academic Record (‘Premio José Cuena’) and Best Master’s Thesis. He academic results lend him be beneficiary of one of the ‘Ayudas Para la Formación de Profesorado Universitario (FPU)’, funded by the Spanish Government. He is currently carrying out his doctoral thesis at the Universidad Rey Juan Carlos, supervised by Professors Abraham Duarte and Eduardo G. Pardo. His main research interests focus on the interface among Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristics procedures for optimization problems modeled by graphs.
Eduardo García Pardo
Full Professor
One of the founders of the investigation group GRAFO, whose main line of research is the development of algorithms to tackle optimization problems, the topic of the researcher’s Doctoral Thesis and which their most notable publications are framed.