A hybrid population-based ruin-and-recreate algorithm for the blocking flow shop scheduling problem
Ewerton Teixeira,
Anand Subramanian and
Hugo Harry Kramer
European Journal of Operational Research, 2026, vol. 329, issue 2, 391-404
Abstract:
This study addresses the blocking flow shop scheduling problem with makespan minimization. In this problem, n jobs need to be scheduled in a serially ordered environment of m machines, where all jobs must follow the same processing order. Additionally, there is no intermediate buffer between these machines. To solve it, we devise a hybrid population-based heuristic, combining a ruin-and-recreate operator with a local search based on variable neighborhood descent. The algorithm incorporates search acceleration procedures, an efficient tie-breaking criterion, and a population diversity control mechanism. Computational experiments were conducted on 150 benchmark instances, and the proposed method achieved highly competitive results, equaling or improving 94.67% the best-known solutions. We also examine the impact of the main components of the algorithm to evaluate their relevance in the performance of the heuristic.
Keywords: Scheduling; Blocking flow shop; Population search; Ruin-and-recreate; Variable neighborhood descent (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221725006149
Full text for ScienceDirect subscribers only
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:eee:ejores:v:329:y:2026:i:2:p:391-404
DOI: 10.1016/j.ejor.2025.08.002
Access Statistics for this article
European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati
More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().