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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
Effective date (Start): October 01, 2023
Effective date (End): September 30, 2025
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)

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