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This paper provides a novel intelligent scheduling strategy for a real-world transportation dynamic scheduling case from an engine workshop of general motor company (GMEW), which is a key production line throughout the manufacturing process. In order to reduce the carbon emission in the scheduling process and make up for ignoring the energy consumption of each part in the scheduling when optimizing the carbon emission of the workshop and the factory. This paper first formulates a fuzzy random chance-constrained programming model of inverse scheduling problem (ISP) with energy consumption. A multi-strategy parallel genetic algorithm based on machine learning (RL-MSPGA) is proposed, which uses machine learning to improve the genetic algorithm. First, the parallel idea is developed to accelerate the process of evolution of genetic algorithm, and the initial population is divided into clusters by  …