Journal of System Simulation
Abstract
Abstract: For the characteristics of multi-variety, large-scale and mixed-flow production of reentrant manufacturing systems, the reentrant hybrid flow shop scheduling problem with batch processors (BPRHFSP) is constructed, and an improved multi-objective mayfly algorithm (MOMA) is proposed for BPRHFSP. Firstly, decoding rules for single-piece processing stage and batch-processing stage are proposed. Then, a reverse learning initialization strategy based on logistic chaotic mapping is designed to improve the quality of the initial solution of the algorithm, also an improved mayfly mating strategy is designed to improve the local search ability of MOMA. Finally, a VND-based mayfly movement strategy is designed based on the coding rule to ensure the quality of the population evolves in a good direction. Through the simulation experiments of a large number of test studies of different scales, it is verified that MOMA is more effective and superior than the traditional algorithm in solving BP-RHFSP. The proposed model can reflect the basic characteristics of production, reducing the makespan, machine load, and carbon emissions.
Recommended Citation
Qin, Hongbin; Li, Chenxiao; Tang, Hongtao; and Zhang, Feng
(2024)
"Reentrant Hybrid Flow Shop Scheduling Problem Based on MOMA,"
Journal of System Simulation: Vol. 36:
Iss.
1, Article 10.
DOI: 10.16182/j.issn1004731x.joss.22-1038
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss1/10
First Page
131
Last Page
148
CLC
TP301.6; TP391.9
Recommended Citation
Qin Hongbin, Li Chenxiao, Tang Hongtao, et al. Reentrant Hybrid Flow Shop Scheduling Problem Based on MOMA[J]. Journal of System Simulation, 2024, 36(1): 131-148.
DOI
10.16182/j.issn1004731x.joss.22-1038
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