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| 7.49 MB | Adobe PDF |
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Abstract(s)
Esta pesquisa investiga a aplicação de metodologias computacionais na indústria madeireira, com foco no Problema do Corte de Material (PCE) com duas iterações: guilhotinável e não guilhotinável. O estudo aplica um algoritmo evolucionário baseado no Non-dominated Sorting Genetic Algorithm II (NSGA-II) adaptado às complexidades do problema para otimizar o processo de corte. A metodologia tem como objetivo melhorar a eficiência da utilização de material em tarefas de trabalho em madeira, empregando este algoritmo utilizando sobras de peças ao invés de uma nova placa. O relatório fornece dados empíricos e métricas de desempenho do algoritmo, demonstrando a sua eficácia na redução do desperdício e na otimização do trabalho na indústria. Esta abordagem melhora a eficiência operacional e sublinha os benefícios ambientais da utilização mais sustentável dos recursos de madeira, exemplificando o potencial da integração de técnicas computacionais em indústrias tradicionais para atingir este objetivo.
This research investigates the application of computational methodologies in the woodworking industry, focusing on the Cutting Stock Problem (CSP) with two iterations: guillotinable and non-guillotinable iterations. The study applies an Evolutionary Algorithm (EA) based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) customized to fit the intricacies of the problem to optimize the cutting process. The methodology aims to enhance material usage efficiency in woodworking tasks by employing this algorithm using leftover parts instead of a new board. The report provides empirical data and performance metrics of the algorithm, demonstrating its effectiveness in reducing waste and optimizing labor in the industry. This approach improves operational efficiency and underscores the environmental benefits of using timber resources more sustainably, exemplifying the potential of integrating computational techniques in traditional industries to achieve this objective.
This research investigates the application of computational methodologies in the woodworking industry, focusing on the Cutting Stock Problem (CSP) with two iterations: guillotinable and non-guillotinable iterations. The study applies an Evolutionary Algorithm (EA) based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) customized to fit the intricacies of the problem to optimize the cutting process. The methodology aims to enhance material usage efficiency in woodworking tasks by employing this algorithm using leftover parts instead of a new board. The report provides empirical data and performance metrics of the algorithm, demonstrating its effectiveness in reducing waste and optimizing labor in the industry. This approach improves operational efficiency and underscores the environmental benefits of using timber resources more sustainably, exemplifying the potential of integrating computational techniques in traditional industries to achieve this objective.
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Keywords
Otimização Otimização multi-objetivo Problemas de empacotamento 2D Problemas de corte de material 2D