Teaching-learning-based optimization algorithm for hybrid flow shop scheduling problem with assembly operations

This paper addresses assembly-type hybrid flow shop scheduling problem (ALIFSP) with the minimi- zation of total tardiness and makespan. The products have parts which arc first processed in hybrid flow shop and then arc assembled at the assembly stage. To solve this problem, a part-based solution representation is adopted and a teachers' teaching-leaming-based optimization (TTLBO) is proposed. Teachers chosen from a non-dominated set undergo a self-learning phase and a teaching phase and these phases are implemented by multiple neighborhood search and global search, respectively. More- over, the learning phase of leamers is removed and the learning of teachers is intensified to avoid the low-efficiency computation. Extensive experiments are conducted to test the performance of TTLBO. The computational results demonstrate that TTLBO is an effective and competitive method f1a, multi- objective AHFSP.