Mathematical models and decomposition algorithms for makespan minimization in plastic rolls production
Vitor Nesello,
Maxence Delorme,
Manuel Iori and
Anand Subramanian
Journal of the Operational Research Society, 2018, vol. 69, issue 3, 326-339
Abstract:
We study an optimization problem that originates from the packaging industry, and in particular in the process of blown film extrusion, where a plastic film is used to produce rolls of different dimensions and colors. The film can be cut along its width, thus producing multiple rolls in parallel, and setup times must be considered when changing from one color to another. The optimization problem that we face is to produce a given set of rolls on a number of identical parallel machines by minimizing the makespan. The problem combines together cutting and scheduling decisions and is of high complexity. For its solution, we propose mathematical models and heuristic algorithms that involve a nontrivial decomposition method. By means of extensive computational experiments, we show that proven optimality can be achieved only on small instances, whereas for larger instances good quality solutions can be obtained especially by the use of an iterated local search algorithm.
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (7)
Downloads: (external link)
http://hdl.handle.net/10.1057/s41274-017-0221-8 (text/html)
Access to full text is restricted to subscribers.
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:taf:tjorxx:v:69:y:2018:i:3:p:326-339
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20
DOI: 10.1057/s41274-017-0221-8
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald
More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().