Multi-population genetic algorithm with greedy job insertion inter-factory neighbourhoods for multi-objective distributed hybrid flow-shop scheduling with unrelated-parallel machines considering tardiness

Hanghao Cui, Xinyu Li, Liang Gao and Chunjiang Zhang

International Journal of Production Research, 2024, vol. 62, issue 12, 4427-4445

Abstract: Distributed manufacturing is gradually becoming the future trend. The fierce market competition makes manufacturing companies focus on productivity and product delivery. The hybrid flow shop scheduling problem (HFSP) is common in manufacturing. Considering the difference of machines at the same stage, the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines (MODHFSP-UPM) is studied with minimum makespan and total tardiness. An improved multi-population genetic algorithm (IMPGA) is proposed for MODHFSP-UPM. The neighbourhood structure is essential for meta-heuristic-based solving algorithms. The greedy job insertion inter-factory neighbourhoods and corresponding move evaluation method are designed to ensure the efficiency of local search. To enhance the optimisation ability and stability of IMPGA, sub-regional coevolution among multiple populations and re-initialisation procedure based on probability sampling are designed, respectively. In computational experiments, 120 instances (including the same proportion of medium and large-scale problems) are randomly generated. The IMPGA performs best in all indicators (spread, generational distance, and inverted generational distance), significantly outperforming existing efficient algorithms for MODHFSP-UPM. Finally, the proposed method effectively solves a polyester film manufacturing case, reducing the makespan and total tardiness by 40% and 60%, respectively.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2023.2262616 (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:12:p:4427-4445

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

DOI: 10.1080/00207543.2023.2262616

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 ().