A hybrid fluid master–apprentice evolutionary algorithm for large-scale multiplicity flexible job-shop scheduling with sequence-dependent set-up time

Abstract

In this article, a large-scale multiplicity flexible job-shop scheduling problem (FJSP) with sequence-dependent set-up time is studied. In this problem, the large production demand for each type of job yields the large-scale multiplicity manufacturing feature. To address the problem, a hybrid fluid master–apprentice evolutionary algorithm (HFMAE) is presented to minimize the makespan. In the first step, a fluid relaxation initialization method (FRI) and an initialize procedure are proposed to obtain high-quality initial solutions. In the FRI, an online fluid tracking policy is presented to improve the assignment decision and the sequencing decision of operations. In the second step, an improved master–apprentice evolutionary method (IMAE) is presented based on the generated initial solutions. In the IMAE, two neighbourhood structures and three makespan estimation approaches are presented to accelerate the solution space search efficiency. Numerical results show that the proposed HFMAE outperforms the comparison algorithms in solving large-scale multiplicity FJSPs.

KEYWORDS:

• Flexible job-shop scheduling
• fluid model
• hybrid fluid master–apprentice evolutionary algorithm
• large-scale optimization
• sequence-dependent set-up time

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are openly available at https://doi.org/10.5281/zenodo.7272914.

Additional information

Funding

This work was supported by the Youth Program of the National Natural Science Foundation of China [grant number 51905196], the National Key R&D Program of China [grant number 2018YFB1702700], the International and Hong Kong, Macao and Taiwan High End Talent Exchange Funding of Guangdong Province [grant number 2022A0505020007], the Guangzhou Basic and Applied Basic Research Project [grant number 202201010261] and the National Natural Science Foundation of China [grant number 71620107002].