JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 35 Issue 6
December 2020
Article Contents
YANG Xingbo, LUO Guofu, SONG Junjie, et al. Research on low carbon scheduling of multi-objective flexible job shop[J]. Journal of Light Industry, 2020, 35(6): 93-99. doi: 10.12187/2020.06.011
Citation: YANG Xingbo, LUO Guofu, SONG Junjie, et al. Research on low carbon scheduling of multi-objective flexible job shop[J]. Journal of Light Industry, 2020, 35(6): 93-99. doi: 10.12187/2020.06.011 shu

Research on low carbon scheduling of multi-objective flexible job shop

  • Received Date: 2020-05-09
  • Aiming at the low carbon job shop scheduling problem, a low carbon scheduling model of multi-objective flexible job shop based on genetic variable neighborhood hybrid algorithm was proposed. When calculating the carbon emissions of mechanical processing system, the carbon emissions directly generated by machine energy consumption and indirect carbon emissions from waste treatment were comprehensively considered. The objective functions of completion time, total machine load and carbon emissions were established. The multi-objective weighting method was used to sum up the objective functions. Finally, the genetic variable neighborhood hybrid algorithm was used to optimize the scheduling model. The simulation results showed that the model could reduce carbon emissions in the production process under the premise of ensuring the balance of completion time and equipment utilization, which verified the feasibility and effectiveness of the model.
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