A genetic algorithm for order acceptance and scheduling in additive manufacturing

Maaz Saleem Kapadia, Reha Uzsoy, Binil Starly and Donald P. Warsing

International Journal of Production Research, 2022, vol. 60, issue 21, 6373-6390

Abstract: We consider the problem of order acceptance and scheduling faced by an additive manufacturing facility consisting of multiple build chambers and postprocessing operations for support removal and surface finishing. We model each build chamber as a batch processing machine with processing times determined by the nesting and orientation of parts within the chamber. Due to the difficulty of developing an explicit functional relation between part batching, batch processing time, and postprocessing requirements we develop random-keys based genetic algorithms to select orders for complete or partial acceptance and produce a high-quality schedule satisfying all technological constraints, including part orientation and rotation within the build chamber. Extensive computational experiments show that the proposed approaches yield significant improvements in profit over the situation where all orders must be accepted, and produce solutions that compare favourably to statistically estimated bounds.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.1991023 (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:tprsxx:v:60:y:2022:i:21:p:6373-6390

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2021.1991023

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().