Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions

Wu C-C, Y Yin and Cheng S-R
Additional contact information
Wu C-C: Feng Chia University, Taichung, Taiwan
Y Yin: East China Institute of Technology, Jiangxi, China
Cheng S-R: Cheng Shiu University, Kaohsiung County, Taiwan

Journal of the Operational Research Society, 2013, vol. 64, issue 1, 147-156

Abstract: Scheduling with learning effects has received growing attention nowadays. A well-known learning model is called ‘position-based learning’ in which the actual processing time of a job is a non-increasing function of its position to be processed. However, the actual processing time of a given job drops to zero precipitously as the number of jobs increases. Motivated by this observation, we propose two truncated learning models in single-machine scheduling problems and two-machine flowshop scheduling problems with ordered job processing times, respectively, where the actual processing time of a job is a function of its position and a control parameter. Under the proposed learning models, we show that some scheduling problems can be solved in polynomial time. In addition, we further analyse the worst-case error bounds for the problems to minimize the total weighted completion time, discounted total weighted completion time and maximum lateness.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.palgrave-journals.com/jors/journal/v64/n1/pdf/jors201246a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/jors/journal/v64/n1/full/jors201246a.html Link to full text HTML (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:64:y:2013:i:1:p:147-156

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

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 ().