The integrated lot-sizing and cutting stock problem under demand uncertainty

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Curcio, E (Autor) Outra; de Lima, VL (Autor) Outra; Miyazawa, FK (Autor) Outra; Silva, E (Autor) Outra; Pedro Amorim (Autor) FEUP

Revista

Título: International Journal of Production Research

Vol. 61

Páginas: 6691-6717

ISSN: 0020-7543

Editora: Taylor & Francis

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00X-FZE

DOI: 10.1080/00207543.2022.2136279

Abstract (EN): Interest in integrating lot-sizing and cutting stock problems has been increasing over the years. This integrated problem has been applied in many industries, such as paper, textile and furniture. Yet, there are only a few studies that acknowledge the importance of uncertainty to optimise these integrated decisions. This work aims to address this gap by incorporating demand uncertainty through stochastic programming and robust optimisation approaches. Both robust and stochastic models were specifically conceived to be solved by a column generation method. In addition, both models are embedded in a rolling-horizon procedure in order to incorporate dynamic reaction to demand realisation and adapt the models to a multistage stochastic setting. Computational experiments are proposed to test the efficiency of the column generation method and include a Monte Carlo simulation to assess both stochastic programming and robust optimisation for the integrated problem. Results suggest that acknowledging uncertainty can cut costs by up to 39.7%, while maintaining or reducing variability at the same time.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 27