Journal of System Simulation
Abstract
Abstract: Aiming at the flexible job shop scheduling problem with AGV (automated guided vehicle), a dual resource integrated scheduling optimization model with the objective of minimizing makespan is established. In the process of population initialization, a heuristic initialization method is proposed to improve the quality of population initial solution and accelerate the convergence speed of the algorithm. A hybrid discrete particle swarm optimization algorithm that can effectively avoid premature maturation is proposed by combining the competitive learning mechanism and the random restart mechanism to address the disadvantages of discrete particle swarm algorithms that are prone to premature maturation. Simulation experiments are carried out on the baseline data set of flexible job shop scheduling considering job transport. The results show that heuristic initialization method and hybrid discrete particle swarm optimization algorithm are feasible and efficient in solving such problems.
Recommended Citation
Chen, Kui; Bi, Li; and Wang, Wenya
(2022)
"Research on Integrated Scheduling of AGV and Machine in Flexible Job Shop,"
Journal of System Simulation: Vol. 34:
Iss.
3, Article 4.
DOI: 10.16182/j.issn1004731x.joss.20-0796
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss3/4
First Page
461
Revised Date
2020-12-14
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0796
Last Page
469
CLC
TP391.9
Recommended Citation
Kui Chen, Li Bi, Wenya Wang. Research on Integrated Scheduling of AGV and Machine in Flexible Job Shop[J]. Journal of System Simulation, 2022, 34(3): 461-469.
DOI
10.16182/j.issn1004731x.joss.20-0796
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