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


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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.
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Key words
Flexible job-shop scheduling,fluid model,hybrid fluid master-apprentice evolutionary algorithm,large-scale optimization,sequence-dependent set-up time
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