Journal of System Simulation
Abstract
Abstract: In order to reduce cost losses caused by delivery delays, distributed heterogeneous hybrid flowshop scheduling problems under combined buffer conditions of finite buffer and zero-wait were studied. A hybrid estimation of distribution algorithm based on Q-learning was proposed to minimize total weighted earliness and tardiness. For the combined buffer, dynamic decoding was designed based on the average factory allocation strategy and the shortest path method. The initial job group was optimized by reverse learning. Q-learning was embedded in the probabilistic model for intelligent searching and updating based on the group state. Reconstruction of the job group was completed using Chebyshev chaotic mapping to improve the group quality. Simulation results show that the proposed algorithm performs well in solving distributed heterogeneous hybrid flow-shop scheduling problems under combined buffer conditions, with transportation time taken into account.
Recommended Citation
Xuan, Hua; Lü, Lin; and Li, Bing
(2025)
"Distributed Heterogeneous Hybrid Flow-shop Scheduling Considering Combined Buffer,"
Journal of System Simulation: Vol. 37:
Iss.
10, Article 20.
DOI: 10.16182/j.issn1004731x.joss.24-0510
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss10/20
First Page
2672
Last Page
2686
CLC
TP49
Recommended Citation
Xuan Hua, Lü Lin, Li Bing. Distributed Heterogeneous Hybrid Flow-shop Scheduling Considering Combined Buffer[J]. Journal of System Simulation, 2025, 37(10): 2672-2686.
DOI
10.16182/j.issn1004731x.joss.24-0510
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