A hybrid white shark optimizer for low-carbon permutation flow shop scheduling.

The most studied optimization objectives in most scheduling problems are cost and quality performance. Due to energy depletion and rising pollution levels, practitioners and researchers are increasingly interested in "low carbon scheduling" as a new scheduling model. The purpose of this research is to develope a Hybrid White Shark Optimizer (WSO) procedure for solving the low-carbon permutation flow shop scheduling (LCPFSP) problem. The proposed algorithm was created by combining the WSO and NEH procedures. Three experimental variations were used to evaluate the algorithm's performance with different population variations and iterations. The results show that increasing population and iterations can reduce LCPFSP carbon emissions. The algorithm comparison results show that the proposed procedure outperforms the Ant Lion Optimizer (ALO) and teaching-learning-based optimization algorithm (TLBOA) procedures. [ABSTRACT FROM AUTHOR]