An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time

W. Qin, J. Zhang () and D. Song
Additional contact information
W. Qin: Shanghai Jiao Tong University
J. Zhang: Shanghai Jiao Tong University
D. Song: Shanghai Jiao Tong University

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 4, No 10, 904 pages

Abstract: Abstract In this article the scheduling problem of dynamic hybrid flow shop with uncertain processing time is investigated and an ant colony algorithm based rescheduling approach is proposed. In order to reduce the rescheduling frequency the concept of due date deviation is introduced, according to which a rolling horizon driven strategy is specially designed. Considering the importance of computational efficiency in the dynamic environment, the traditional ant colony optimization is improved. On the one hand, a strategy of available routes compression to restrict ants’ movement is proposed so that the ants’ searching cycle for new solutions could be shorten. On the other hand, illuminating function in state transfer possibility is improved to facilitate the exploration of low pheromone trail. Performance of rolling horizon procedure and rescheduling algorithm are evaluated respectively through simulations, the results show the best parameters of rolling horizon procedure and demonstrate the feasibility and efficiency of rescheduling algorithm. An example from the practical production is addressed to verify the effectiveness of the proposed approach.

Keywords: Hybrid flowshop; Uncertain processing time; Ant colony algorithm; Rolling rescheduling strategy (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1144-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1144-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-015-1144-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().