A modified memetic algorithm with multi-operation precise joint movement neighbourhood structure for the assembly job shop scheduling problem

Zhiyong Ba, Yiping Yuan and Jinduo Liu

International Journal of Production Research, 2024, vol. 62, issue 17, 6292-6324

Abstract: This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2024.2313087 (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:17:p:6292-6324

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

DOI: 10.1080/00207543.2024.2313087

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 ().