A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems

Roland Braune, Frank Benda, Karl F. Doerner and Richard F. Hartl

International Journal of Production Economics, 2022, vol. 243, issue C

Abstract: This paper deals with a Genetic Programming (GP) approach for solving flexible shop scheduling problems. The adopted approach aims to generate priority rules in the form of an expression tree for dispatching jobs. Therefore, in a list-scheduling algorithm, the available jobs can be ranked using the tree-based priority rules generated using GP.

Keywords: Flexible shop scheduling; Genetic programming; Machine learning; Iterative dispatching rule; Multi-tree representation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0925527321003182
Full text for ScienceDirect subscribers only

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:eee:proeco:v:243:y:2022:i:c:s0925527321003182

DOI: 10.1016/j.ijpe.2021.108342

Access Statistics for this article

International Journal of Production Economics is currently edited by Stefan Minner

More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().