Low carbon emission management for flexible job shop scheduling: a study case in China

Abstract

For flexible job-shop scheduling problem with low carbon emission constraints, an improved quantum genetic algorithm based on double chains coding is proposed. Firstly, a mathematical model is established to minimize makespan, total workload of machines and carbon emissions of machines. Secondly, carbon emission equations in job shop scheduling process are inducted and designed. Based on the selected model, a method using an improved quantum genetic algorithm with double chains to solve processing route selection is proposed. Finally, on the basis of Kacem example, the performance of the method proposed in the paper was analyzed by ANOVA through experimental simulation and compared with the algorithms commonly used at present. The results show that the method not only achieves the goal of optimization, but also meets the practical requirements of reducing carbon emissions in production and processing.

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Acknowledgements

Foundation items: Project supported by Liaoning Provincial Natural Science Foundation (20180550499, 2019-ZD-0109) and Education Department Project of Liaoning Province (JDL2019022).

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Correspondence to Tao Ning.

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Ning, T., Huang, Y. Low carbon emission management for flexible job shop scheduling: a study case in China. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03330-6

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Keywords

  • Flexible job-shop scheduling
  • Carbon emission
  • Double chains quantum encoding
  • Improved quantum genetic algorithm