Research on Low-carbon Flexible Workshop Scheduling Based on Improved Whale Optimization Algorithm
In this paper,a scheduling model with dual objectives of maximizing completion time and minimizing carbon emissions is established to address issues such as completion time or equipment utilization for flexible manufacturing workshops.Due to slow convergence and susceptibility to local optima,a modified whale optimization algorithm is proposed using a search method that combines inertia weight and chaotic disturbance convergence factor in this paper.The algorithm is designed based on the characteristics of workshop scheduling,adopting a two-segment equal-length coding method to accelerate the transformation speed between machine selection and individual position;a hybrid initialization population is used to improve population diversity;inertia weight and chaotic disturbance convergence factor are incorporated to balance algorithm search capability;and a polynomial mutation strategy is introduced to help the algorithm escape local optima.The algorithm's search capability is compared with genetic algorithms and traditional whale algorithms under test functions,verifying its superiority.Furthermore,through practical examples,the effectiveness of the improved whale optimization algorithm is further demonstrated.
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