With the increasingly fierce global competition, the manufacturing industry also needs to implement low-carbon scheduling to improve its competitiveness. To achieve the low-carbon goals of manufacturing enterprises, this study first constructs a multi-objective workshop low-carbon scheduling model for manufacturing enterprises. Then, the crossover operator, mutation operator, and elite retention strategy of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are improved, which is applied to handle the low-carbon scheduling model between vehicles. When the targets were 20, the proposed model solved two multi-objective optimization test functions with inverse generation distance values of 0.338 and 1.153, and spatial evaluation values of 0.013 and 0.415. The proposed model had a faster solving speed and converged to the optimal solution in about 10 iterations. The proposed model performed the best in solving Low-Carbon Scheduling in Manufacturing Workshops (LSCW), with the shortest maximum completion time of 6.12 hours, the lowest total energy consumption of 6.71×105 kJ, and still the lowest carbon emissions of 5.92×104 kW/h. The proposed model in solving the low-carbon scheduling model of manufacturing workshops can help reduce carbon emissions in manufacturing workshops and promote the green transformation of the manufacturing industry.

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