To achieve energy conservation and emission reduction, the scope of the flexible job-shop scheduling problem (FJSP) has been broadened to address the green flexible job-shop scheduling problem (GFJSP). However, solely focusing on scheduling doesn't adequately achieve green production. Furthermore, dynamic events in the job-shop necessitate rescheduling to resume production. Considering these, this paper proposes a scheduling optimization method for a GFJSP under dynamic events (DGFJSP). Firstly, the method introduces an optimization model that considers the switching on/off strategy of machines in idle and the electric power cost under time-of-use pricing. Secondly, the method employs a multi-objective genetic algorithm based on variable neighborhood search (MOGV), which introduces an adaptive elite retention strategy based on external archive and adaptive operators. The efficiency and competitiveness of the MOGV are verified by the benchmark instance, and the MOGV is applied to rescheduling. Ultimately, the method introduces the green flexible job-shop rescheduling metrics: productivity, energy conservation, robustness, and stability as the decision-making basis of the combined rescheduling method. Total rescheduling based on MOGV reduces the value of the comprehensive indicators by 5.931% when machine breakdown and by 6.033% when order insertion or defective return compared to right-shift rescheduling.
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