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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Curcio, E (Author) Other; de Lima, VL (Author) Other; Miyazawa, FK (Author) Other; Silva, E (Author) Other; Pedro Amorim (Author) FEUP

Journal

Title: International Journal of Production Research

Vol. 61

Pages: 6691-6717

ISSN: 0020-7543

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 27