An adaptive shuffled frog-leaping algorithm for parallel batch processing machines scheduling with machine eligibility in fabric dyeing process

Deming Lei and Tao Dai

International Journal of Production Research, 2024, vol. 62, issue 21, 7704-7721

Abstract: Fabric dyeing is an important production process in clothing industry and consists of some batch processing machines (BPM). In this study, parallel BPM scheduling problem with machine eligibility in fabric dyeing process is considered and an adaptive shuffled frog-leaping algorithm (ASFLA) is presented to minimise makespan and total tardiness simultaneously. A heuristic is used to produce initial population. Evolution quality of population on the last generation is used to implement adaptive population division and adaptive search process in each memeplex on the current generation. Search process of each memeplex is divided into some segments and search operators are dynamically adjusted after each segment is done. Adaptive elimination is performed on the worst memeplex. Extensive experiments are conducted to test the performance of ASFLA and the computational results demonstrate that new strategies of ASFLA are effective and ASFLA has promising advantages on the considered problem.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2024.2324452 (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:62:y:2024:i:21:p:7704-7721

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

DOI: 10.1080/00207543.2024.2324452

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 ().