Flow-shop scheduling with a learning effect
Wang J-B () and
Xia Z-Q
Additional contact information
Wang J-B: Shenyang Institute of Aeronautical Engineering
Xia Z-Q: Dalian University of Technology
Journal of the Operational Research Society, 2005, vol. 56, issue 11, 1325-1330
Abstract:
Abstract The paper is devoted to some flow-shop scheduling problems with a learning effect. The objective is to minimize one of the two regular performance criteria, namely, makespan and total flowtime. A heuristic algorithm with worst-case bound m for each criteria is given, where m is the number of machines. Furthermore, a polynomial algorithm is proposed for both of the special cases: identical processing time on each machine and an increasing series of dominating machines. An example is also constructed to show that the classical Johnson's rule is not the optimal solution for the two-machine flow-shop scheduling to minimize makespan with a learning effect. Some extensions of the problem are also shown.
Keywords: scheduling; flow shop; learning effect; worst-case analysis (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)
Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jors.2601856 Abstract (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:pal:jorsoc:v:56:y:2005:i:11:d:10.1057_palgrave.jors.2601856
Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274
DOI: 10.1057/palgrave.jors.2601856
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook
More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().