Journal of System Simulation
Abstract
Abstract: In view of the green flexible job shop scheduling problem where machines have multiple speeds, a green flexible job shop scheduling model under multiple speeds was constructed to minimize the makespan and total energy consumption under different speeds. A discrete coronavirus herd immunity optimizer (DCHIO) was proposed for a solution. A discrete individual updating method was introduced for the relatively large solution space of the multi-speed problem, based on which a population updating mechanism with multi-scale joint search was proposed to search the solution space quickly and uniformly. A dynamic mutation operation was designed to enhance the population diversity of the algorithm while realizing the adaptive tuning. By mining the empirical knowledge of the current scheduling scheme, a knowledge-driven neighborhood search strategy was proposed to simultaneously reduce the makespan and energy consumption. The experimental results show that the algorithm proposed in this paper can effectively solve the green flexible scheduling problem under multiple speeds.
Recommended Citation
Ma, Xunde; Bi, Li; and Wang, Junjie
(2024)
"Research on Green Job Shop Scheduling Based on Herd Immunity Optimizer,"
Journal of System Simulation: Vol. 36:
Iss.
11, Article 7.
DOI: 10.16182/j.issn1004731x.joss.23-0896
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss11/7
First Page
2578
Last Page
2591
CLC
TP391.9
Recommended Citation
Ma Xunde, Bi Li, Wang Junjie. Research on Green Job Shop Scheduling Based on Herd Immunity Optimizer[J]. Journal of System Simulation, 2024, 36(11): 2578-2591.
DOI
10.16182/j.issn1004731x.joss.23-0896
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