Journal of System Simulation
Abstract
Abstract: For the traditional flexible job shop scheduling problem, a joint optimization of machine dynamic pre-maintenance and green scheduling is considered to establish an integrated optimization model with the optimization objectives of minimizing maximum completion time, total carbon emissions, and total cost. An improved NSGA-II algorithm is proposed to solve the model. A three-layer encoding method based on process, machine, and pre maintenance is adopted to design a one-step decoding scheme that considers process allocation, machine selection, and machine pre-maintenance strategies. The algorithm improves the elitist retention strategy, designs an adaptive crossover mutation function with algebraic changes, and a mutation operator based on neighborhood search. The experiment validates the effectiveness of the improved algorithm in solving scheduling problems of different scales, the proposed dynamic pre-maintenance strategy can more effectively solve the collaborative optimization problem of pre-maintenance and flexible job shop green scheduling compared to other maintenance strategies.
Recommended Citation
Jiang, Yuyan; Ma, Ning; Li, Yan; Gan, Rumeijiang; and Wang, Fuyu
(2025)
"Collaborative Optimization Problem of Dynamic Pre-maintenance and Green Scheduling,"
Journal of System Simulation: Vol. 37:
Iss.
2, Article 5.
DOI: 10.16182/j.issn1004731x.joss.23-1198
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss2/5
First Page
362
Last Page
378
CLC
TP391.9; TP301.6
Recommended Citation
Jiang Yuyan, Ma Ning, Li Yan, et al. Collaborative Optimization Problem of Dynamic Pre-maintenance and Green Scheduling[J]. Journal of System Simulation, 2025, 37(2): 362-378.
DOI
10.16182/j.issn1004731x.joss.23-1198
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