A hybrid approach for the dynamic flexible job shop scheduling problem considering machine failures

Chong Peng (), Zhongwen Zhang (), T. Warren Liao (), Hui Zhao () and Yuzhen Cai ()
Additional contact information
Chong Peng: Beihang University
Zhongwen Zhang: Beihang University
T. Warren Liao: Louisiana State University
Hui Zhao: Beihang University
Yuzhen Cai: Beihang University

Journal of Scheduling, 2025, vol. 28, issue 4, No 3, 407-424

Abstract: Abstract In practical production scheduling, dynamic disturbances such as machine failures frequently disrupt initial schedules. In this research, a new approach using a genetic algorithm prescheduling and machining path routing strategy is proposed to solve the dynamic flexible job shop scheduling problem. Firstly, the efficiency of the scheduling algorithm is improved by a genetic algorithm with an improved active decoding method and a rescheduling algorithm with a dual strategy of right shift and processing path rerouting. Then, a more reasonable solution is obtained by path rerouting in the framework of a prescheduling strategy using a binary tree-based identification system to determine the set of affected processes to reduce the restriction on alternative paths while increasing the search range. Finally, the proposed rescheduling algorithm is compared with two methods through experimental comparisons, which confirms that the algorithm can obtain a more robust and stable solution.

Keywords: Dynamic scheduling; Flexible job shop; Machine failure; Genetic algorithm; Machining path routing strategy (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10951-025-00839-y 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:jsched:v:28:y:2025:i:4:d:10.1007_s10951-025-00839-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10951

DOI: 10.1007/s10951-025-00839-y

Access Statistics for this article

Journal of Scheduling is currently edited by Edmund Burke and Michael Pinedo

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