Thèse de Doctorat
DOI
Document
Auteur
Nom complet
Larissa Tebaldi de Oliveira
Unité de l'USP
Domain de Connaissance
Date de Soutenance
Editeur
São Carlos, 2019
Directeur
Jury
Toledo, Franklina Maria Bragion de (Président)
Andretta, Marina
Usberti, Fabio Luiz
Titre en portugais
Uma integração dos problemas de empacotamento de peças irregulares e de caminho mínimo de corte
Mots-clés en portugais
Caminho de corte
Empacotamento de peças irregulares
Matheurísticas
Resumé en portugais
Titre en anglais
Integrating nesting and cutting path determination problems
Mots-clés en anglais
Cutting path
Integrated models
Irregular packing problem
Matheurísticas
Resumé en anglais
Having great applicability in industries, ranging from small clothing industries to large metal mechanic ones, packing problems aim to determine the positioning of small pieces over a large object minimizing, for instance, raw material waste. The main characteristic and obstacle of the irregular strip packing problem, studied in this research, is the irregular shape of its pieces. In some industries, after a layout of pieces has been defined, a second problem arises: the cutting path determination problem. Although the solution of the first strongly influences the resolution of the second, to the best of our knowledge, there are no strategy to integrate these problems. Here, we propose two irregular strip packing and cutting path integrated models. The first one minimizes the cutting path between the pieces considering that the cutting starts at a fixed vertex for each piece, while the second considers the cutting start point in any vertex of the pieces. Computational tests show that it is advantageous to integrate the problems, however, as both are difficult to solve, the integrated one is at least as difficult as each of them, so only small instances were solved to optimality. A matheuristic, based on the biased random-key genetic algorithm, is proposed for the continuos irregular strip packing problem and then extended to the integrated problem. The results are promising, the matheuristics is able to find solution for instances that had not been solved through the previously proposed integrated models.

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Date de Publication
2019-06-13

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