Mathematical models and adaptive solution methods for the multi-period cutting stock problem with setups and capacity constraints

• Grant number: 23/04588-4
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: October 01, 2023
• End date: September 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Mathematics - Applied Mathematics
• Principal Investigator: Antônio Augusto Chaves
• Grantee: Eduardo Machado Silva
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil

Abstract

Lot sizing and cutting-stock problems play an important role in several industrial sectors, such as furniture, paper, and metallurgical factories. The literature has shown better results when integrating both problems rather than dealing with them separately. These problems are NP-hard, so heuristic and metaheuristic approaches are interesting alternatives to finding good solutions in a reasonable time. A decisive factor in developing metaheuristics is parameter tuning. The parameter settings are usually not optimal for all instances, so adaptive configuration techniques are interesting to obtain more accurate results. This project aims to study mathematical models and develop an adaptive metaheuristic based on machine learning concepts for the multi-period cutting stock problem with capacity constraints and setups on the cutting patterns. (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)

VIEIRA, BRUNO SALEZZE; SILVA, EDUARDO MACHADO; CHAVES, ANTONIO AUGUSTO. Random-key algorithms for optimizing integrated Operating Room Scheduling. APPLIED SOFT COMPUTING, v. 180, p. 25-pg., 2025-08-01. (22/05803-3, 18/15417-8, 23/04588-4, 21/09482-4, 24/08848-3)