The two-dimensional cutting stock problem with usable leftovers: mathematical modelling and heuristic approaches

Douglas Nogueira Nascimento (), Adriana Cristina Cherri () and José Fernando Oliveira ()
Additional contact information
Douglas Nogueira Nascimento: São Paulo State University (UNESP)
Adriana Cristina Cherri: São Paulo State University (UNESP)
José Fernando Oliveira: University of Porto

Operational Research, 2022, vol. 22, issue 5, No 22, 5363-5403

Abstract: Abstract Different variations of the classic cutting stock problem (CSP) have emerged and presented increasingly complex challenges for scientists and researchers. One of these variations, which is the central subject of this work, is the two-dimensional cutting stock problem with usable leftovers (2D-CSPUL). In these problems, leftovers can be generated to reduce waste. This technique has great practical importance for many companies, with a strong economic and environmental impact. In this paper, a non-linear mathematical model and its linearization are proposed to represent the 2D-CSPUL. Due to the complexity of the model, a heuristic procedure was also proposed. Computational tests were performed with instances from the literature and randomly generated instances. The results demonstrate that the proposed model and the heuristic procedure satisfactorily solve the problem, proving to be adequate and beneficial tools when applied to real situations.

Keywords: Two-dimensional cutting stock problem; Usable leftovers; Mathematical modelling; Exact methods; Heuristic procedure (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s12351-022-00735-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00735-9

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-022-00735-9

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().